From e612abb172da9944301e3e9b4826ea8427bcc570 Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Tue, 19 Jun 2018 18:55:06 +0100 Subject: [PATCH 1/9] WIP: Implement DDPG --- demo/webapp/level2.html | 8 +- demo/webapp/public/js/DDPG/ddpg.js | 40 ++++ .../public/js/{level2.js => DDPG/index.js} | 3 + demo/webapp/public/js/DDPG/memory.js | 77 ++++++++ demo/webapp/public/js/DDPG/models.js | 185 ++++++++++++++++++ demo/webapp/public/js/DDPG/noise.js | 41 ++++ 6 files changed, 353 insertions(+), 1 deletion(-) create mode 100644 demo/webapp/public/js/DDPG/ddpg.js rename demo/webapp/public/js/{level2.js => DDPG/index.js} (89%) create mode 100644 demo/webapp/public/js/DDPG/memory.js create mode 100644 demo/webapp/public/js/DDPG/models.js create mode 100644 demo/webapp/public/js/DDPG/noise.js diff --git a/demo/webapp/level2.html b/demo/webapp/level2.html index 78bf318..3d30368 100644 --- a/demo/webapp/level2.html +++ b/demo/webapp/level2.html @@ -37,11 +37,17 @@

Current state (Lidar points)



+ - + + + + + + diff --git a/demo/webapp/public/js/DDPG/ddpg.js b/demo/webapp/public/js/DDPG/ddpg.js new file mode 100644 index 0000000..1fe09dc --- /dev/null +++ b/demo/webapp/public/js/DDPG/ddpg.js @@ -0,0 +1,40 @@ +// This class is called from js/DDPG/index.js +class DDPG { + + constructor(){ + // Default Config + this.config = { + "stateSize": 25, + "nbActions": 2, + "layerNom": true, + "normalizeObservations": true, + "seed": 0, + "criticL2Reg": 0.01, + "batchSize": 64, + "actorLr": 0.0001, + "criticLr": 0.001, + "gamma": 0.99, + "rewardScale": 1, + "nbEpochs": 500, + "nbEpochsCycle": 800, + "nbTrainSteps": 50, + "nbRolloutStep": 100 + }; + // Inputs + const obsInput = tf.input({shape: [this.config.stateSize]}); + const actionInput = tf.input({shape: [this.config.nbActions]}); + + // From js/DDPG/noise.js + this.paramNoise = new AdaptiveParamNoiseSpec(); + // Buffer replay + // The baseline use 1e6 but this size should be enough + this.memory = new Memory(1000); + // Actor and Critic are from js/DDPG/models.js + this.actor = new Actor( + this.config.stateSize, this.config.nbActions, this.config.layerNom, this.config.seed); + this.critic = new Critic( + this.config.stateSize, this.config.nbActions, this.config.layerNom, this.config.seed); + this.actor.buildModel(obsInput); + this.critic.buildModel(obsInput, actionInput); + } +}; \ No newline at end of file diff --git a/demo/webapp/public/js/level2.js b/demo/webapp/public/js/DDPG/index.js similarity index 89% rename from demo/webapp/public/js/level2.js rename to demo/webapp/public/js/DDPG/index.js index 5b1b5b7..30ebda4 100644 --- a/demo/webapp/public/js/level2.js +++ b/demo/webapp/public/js/DDPG/index.js @@ -1,5 +1,8 @@ let levelUrl = metacar.level.level2; +// js/DDPG/ddpg.js +var ddpg = new DDPG(); + var env = new metacar.env("canvas", levelUrl); env.setAgentMotion(metacar.motion.ControlMotion, {}); diff --git a/demo/webapp/public/js/DDPG/memory.js b/demo/webapp/public/js/DDPG/memory.js new file mode 100644 index 0000000..6b13d58 --- /dev/null +++ b/demo/webapp/public/js/DDPG/memory.js @@ -0,0 +1,77 @@ + +class Memory { + + /** + * @param maxlen (number) Buffer limit + */ + constructor(maxlen){ + this.maxlen = maxlen; + this.length = 0; + this.start = 0; + + this.obs0List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.obs1List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.rewardsList = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.actionsList = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.terminals1List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + } + + /** + * @param idx (number) + */ + getItem(idx){ + if (idx < 0 || idx >= this.length){ + console.error("Memory.getItem: idx not in range."); + } + return this.data[(this.start + idx) % this.maxlen] + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + getBatch(batchSize){ + const arrLength = this.obs0List.length; + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals1': [], + }; + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * arrLength); + batch.obs0.push(this.obs0List[id]); + batch.obs1.push(this.obs1List[id]); + batch.rewards.push(this.rewardsList[id]); + batch.actions.push(this.actionsList[id]); + batch.terminals1.push(this.terminals1List[id]); + } + return batch + } + + /** + * @param obs0 [] + * @param action (number) + * @param reward (number) + * @param obs1 [] + * @param terminal1 (boolean) + */ + append(obs0, action, reward, obs1, terminal1){ + if (this.length < this.maxlen){ + this.length += 1; + } + else if (this.length == this.maxlen) { + this.start = (this.start + 1) % this.maxlen; + } + else { + console.error("Memory.append: This should never be printed"); + } + this.obs0List[(this.start + this.length - 1) % this.maxlen] = obs0; + this.obs1List[(this.start + this.length - 1) % this.maxlen] = action; + this.rewardsList[(this.start + this.length - 1) % this.maxlen] = reward; + this.actionsList[(this.start + this.length - 1) % this.maxlen] = obs1; + this.terminals1List[(this.start + this.length - 1) % this.maxlen] = terminal1; + } +} \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/models.js b/demo/webapp/public/js/DDPG/models.js new file mode 100644 index 0000000..5a97256 --- /dev/null +++ b/demo/webapp/public/js/DDPG/models.js @@ -0,0 +1,185 @@ + +class Actor{ + + /** + * @param stateSize(number) + * @param nbActions (number) + * @param layerNorm (boolean) + * @param seed (number) + */ + constructor(stateSize, nbActions, layerNorm, seed) { + this.stateSize = stateSize; + this.nbActions = nbActions; + this.layerNorm = layerNorm; + this.seed = seed; + } + + /** + * + * @param obs tf.input + */ + buildModel(obs){ + this.firstLayerBatchNorm = null; + this.secondLayerBatchNorm = null; + + this.relu = tf.layers.thresholdedReLU(); + + // First layer with BatchNormalization + this.firstLayer = tf.layers.dense({ + inputShape: this.stateSize, + units: 64, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'linear', // relu is add later + useBias: true, + biasInitializer: "zeros" + }); + if (this.layerNorm){ + // WARNING: BatchNormalization instead of layerNormalization + this.firstLayerBatchNorm = tf.layers.batchNormalization({ + scale: true, + center: true + }); + } + + // Second layer with BatchNormalization + this.secondLayer = tf.layers.dense({ + inputShape: 64, + units: 64, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'linear', // relu is add later + useBias: true, + biasInitializer: "zeros" + }); + if (this.layerNorm){ + // WARNING: BatchNormalization instead of layerNormalization + this.secondLayerBatchNorm = tf.layers.batchNormalization({ + scale: true, + center: true + }); + } + + // Ouput layer + this.outputLayer = tf.layers.dense({ + inputShape: 64, + units: this.nbActions, + kernelInitializer: tf.initializers.randomUniform({ + minval: 0.003, maxval: 0.003, seed: this.seed}), + activation: 'tanh', + useBias: true, + biasInitializer: "zeros" + }); + + // Actor prediction + const predict = () => { + return tf.tidy(() => { + let l1 = this.firstLayer.apply(obs); + if (this.firstLayerBatchNorm){ + l1 = this.firstLayerBatchNorm.apply(l1); + } + //l1 = this.relu.apply(l1); + let l2 = this.secondLayer.apply(l1); + if (this.secondLayerBatchNorm){ + l2 = this.secondLayerBatchNorm.apply(l2); + } + //l2 = this.relu.apply(l2); + return this.outputLayer.apply(l2); + }); + } + const output = predict(); + this.model = tf.model({inputs: this.obs, outputs: output}); + } + +}; + +class Critic { + + /** + * @param stateSize(number) + * @param nbActions (number) + * @param layerNorm (boolean) + * @param seed (number) + */ + constructor(stateSize, nbActions, layerNorm, seed) { + this.stateSize = stateSize; + this.nbActions = nbActions; + this.layerNorm = layerNorm; + } + + /** + * + * @param obs tf.input + * @param action tf.input + */ + buildModel(obs, action){ + this.firstLayerBatchNorm = null; + this.secondLayerBatchNorm = null; + + this.relu = tf.layers.thresholdedReLU(); + + // First layer with BatchNormalization + this.firstLayer = tf.layers.dense({ + inputShape: this.stateSize, + units: 64, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'linear', // relu is add later + useBias: true, + biasInitializer: "zeros" + }); + if (this.layerNorm){ + // WARNING: BatchNormalization instead of layerNormalization + this.firstLayerBatchNorm = tf.layers.batchNormalization({ + scale: true, + center: true + }); + } + + // Second layer with BatchNormalization + this.secondLayer = tf.layers.dense({ + inputShape: 64 + this.nbActions, // Previous layer + action + units: 64, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'linear', // relu is add later + useBias: true, + biasInitializer: "zeros" + }); + if (this.layerNorm){ + // WARNING: BatchNormalization instead of layerNormalization + this.secondLayerBatchNorm = tf.layers.batchNormalization({ + scale: true, + center: true + }); + } + + // Ouput layer + this.outputLayer = tf.layers.dense({ + inputShape: 64, + units: 1, + kernelInitializer: tf.initializers.randomUniform({ + minval: 0.003, maxval: 0.003, seed: this.seed}), + activation: 'tanh', + useBias: true, + biasInitializer: "zeros" + }); + + // Actor prediction + const predict = () => { + return tf.tidy(() => { + let l1 = this.firstLayer.apply(obs); + l1 = l1.concat(action); + if (this.firstLayerBatchNorm){ + l1 = this.firstLayerBatchNorm.apply(l1); + } + //l1 = this.relu(l1); + let l2 = this.secondLayer.apply(l1); + if (this.secondLayerBatchNorm){ + l2 = this.secondLayerBatchNorm.apply(l2); + } + //l2 = this.relu.apply(l2); + return this.outputLayer.apply(l2); + }); + } + const output = predict(); + this.model = tf.model({inputs: this.obs, outputs: output}); + } + +}; diff --git a/demo/webapp/public/js/DDPG/noise.js b/demo/webapp/public/js/DDPG/noise.js new file mode 100644 index 0000000..c173625 --- /dev/null +++ b/demo/webapp/public/js/DDPG/noise.js @@ -0,0 +1,41 @@ +/** + * Noise class + * The original baseline is made of three noise + * AdaptiveParamNoiseSpec, ActionNoise and NormalActionNoise + * Only AdaptiveParamNoiseSpec is implemented for now + * See "C Adapative Scaling" Page 14 in the paper. + */ + +class AdaptiveParamNoiseSpec{ + + /** + * @param conf Object + * conf.initialStddev: 0.1 default // σ + * conf.desiredActionStddev: 0.1 default // δ + * conf.adoptionCoefficient: 1.01 default // α + */ + constructor(conf){ + conf = conf || {}; + this.initialStddev = conf.initialStddev || 0.1; + this.desiredActionStddev = conf.initialStddev || 0.1; + this.adoptionCoefficient = conf.adoptionCoefficient || 1.01; + this.currentStddev = conf.initialStddev; + } + + /** + * The distance from the Adaptive scaling + * @param distance number + */ + adapt(distance){ + // if d(π, _π_) > δ then σ = σ/α + if (distance > this.desiredActionStddev){ + // Decrease σ + this.currentStddev /= this.adoptionCoefficient; + } + else{ + // σ = σ*α + // Increase σ + this.currentStddev *= this.adoptionCoefficient; + } + } +}; \ No newline at end of file From dac1db06ef7e1484a3841f528d848211eec9325d Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Fri, 22 Jun 2018 19:36:29 +0100 Subject: [PATCH 2/9] DDPG Implemented --- demo/webapp/level2.html | 5 + demo/webapp/public/js/DDPG/ddpg.js | 279 ++++++++++++++++++++--- demo/webapp/public/js/DDPG/ddpg_agent.js | 168 ++++++++++++++ demo/webapp/public/js/DDPG/index.js | 36 ++- demo/webapp/public/js/DDPG/memory.js | 16 +- demo/webapp/public/js/DDPG/models.js | 172 ++++++++++---- demo/webapp/public/js/DDPG/noise.js | 4 +- demo/webapp/public/js/viewer.js | 47 ++++ src/car.ts | 38 ++- src/control_motion_engine.ts | 12 +- src/level.ts | 13 +- src/metacar.ts | 16 +- src/ui_event.ts | 1 + src/world.ts | 9 + 14 files changed, 702 insertions(+), 114 deletions(-) create mode 100644 demo/webapp/public/js/DDPG/ddpg_agent.js diff --git a/demo/webapp/level2.html b/demo/webapp/level2.html index 3d30368..d0c93ee 100644 --- a/demo/webapp/level2.html +++ b/demo/webapp/level2.html @@ -37,6 +37,10 @@

Current state (Lidar points)



+
+ + + @@ -48,6 +52,7 @@

Current state (Lidar points)



+ diff --git a/demo/webapp/public/js/DDPG/ddpg.js b/demo/webapp/public/js/DDPG/ddpg.js index 1fe09dc..04e95f9 100644 --- a/demo/webapp/public/js/DDPG/ddpg.js +++ b/demo/webapp/public/js/DDPG/ddpg.js @@ -1,40 +1,251 @@ -// This class is called from js/DDPG/index.js + +function logTfMemory(){ + let mem = tf.memory(); + console.log("numBytes:" + mem.numBytes + + "\nnumBytesInGPU:" + mem.numBytesInGPU + + "\nnumDataBuffers:" + mem.numDataBuffers + + "\nnumTensors:" + mem.numTensors); +} + +// This class is called from js/DDPG/ddpg_agent.js class DDPG { - constructor(){ - // Default Config - this.config = { - "stateSize": 25, - "nbActions": 2, - "layerNom": true, - "normalizeObservations": true, - "seed": 0, - "criticL2Reg": 0.01, - "batchSize": 64, - "actorLr": 0.0001, - "criticLr": 0.001, - "gamma": 0.99, - "rewardScale": 1, - "nbEpochs": 500, - "nbEpochsCycle": 800, - "nbTrainSteps": 50, - "nbRolloutStep": 100 - }; + /** + * @param config (Object) + * @param actor (Actor class) + * @param critic (Critic class) + * @param memory (Memory class) + * @param noise (Noise class) + */ + constructor(actor, critic, memoryPos, memoryNeg, noise, config){ + this.actor = actor; + this.critic = critic; + this.memoryPos = memoryPos; + this.memoryNeg = memoryNeg; + this.noise = noise; + this.config = config; + this.tfGamma = tf.scalar(config.gamma); + // Inputs - const obsInput = tf.input({shape: [this.config.stateSize]}); - const actionInput = tf.input({shape: [this.config.nbActions]}); - - // From js/DDPG/noise.js - this.paramNoise = new AdaptiveParamNoiseSpec(); - // Buffer replay - // The baseline use 1e6 but this size should be enough - this.memory = new Memory(1000); - // Actor and Critic are from js/DDPG/models.js - this.actor = new Actor( - this.config.stateSize, this.config.nbActions, this.config.layerNom, this.config.seed); - this.critic = new Critic( - this.config.stateSize, this.config.nbActions, this.config.layerNom, this.config.seed); + let obsInput = tf.input({batchShape: [null, this.config.stateSize]}); + let actionInput = tf.input({batchShape: [null, this.config.nbActions]}); + + if (config.normalizeObservations){ + tf.layers.batchNormalization({ + scale: true, + center: true + }).apply(obsInput); + } + + // Randomly Initialize actor network μ(s) this.actor.buildModel(obsInput); + // Randomly Initialize critic network Q(s, a) this.critic.buildModel(obsInput, actionInput); + + // Define in js/DDPG/models.js + // Init target network Q' and μ' with the same weights + this.actorTarget = copyModel(this.actor, Actor); + this.criticTarget = copyModel(this.critic, Critic); + // Perturbed Actor (See parameter space noise Exploration paper) + this.perturbedActor = copyModel(this.actor, Actor); + //this.adaptivePerturbedActor = copyModel(this.actor, Actor); + + this.actorOptimiser = tf.train.adam(this.config.actorLr); + this.criticOptimiser = tf.train.adam(this.config.criticLr); + + this.criticWeights = []; + for (let w = 0; w < this.critic.model.weights.length; w++){ + this.criticWeights.push(this.critic.model.weights[w].val); + } + + this.actorWeights = []; + for (let w = 0; w < this.actor.model.weights.length; w++){ + this.actorWeights.push(this.actor.model.weights[w].val); + } + + // Return a batch from positive and negative experiences + this.memory = { + getBatch: (size) => { + let batch1 = this.memoryPos.getBatch(size/2); + let batch2 = this.memoryNeg.getBatch(size/2); + return { + 'obs0': batch1.obs0.concat(batch2.obs0), + 'obs1': batch1.obs1.concat(batch2.obs1), + 'rewards': batch1.rewards.concat(batch2.rewards), + 'actions': batch1.actions.concat(batch2.actions), + 'terminals': batch1.terminals.concat(batch2.terminals), + }; + } + }; + + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev); + } + + + /** + * Distance Measure for DDPG + * See parameter space noise Exploration paper + * obs (Tensor2d) Observations + */ + distanceMeasure(observations) { + return tf.tidy(() => { + const pertubedPredictions = this.perturbedActor.model.predict(observations); + const predictions = this.actor.model.predict(observations); + + const distance = tf.square(pertubedPredictions.sub(predictions)).mean().sqrt(); + return distance; + }); + } + + /** + * AdaptParamNoise + */ + adaptParamNoise(){ + const batch = this.memory.getBatch(this.config.batchSize); + const tfObs0 = tf.tensor2d(batch.obs0); + const distance = this.distanceMeasure(tfObs0); + + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev); + + const distanceV = distance.buffer().values; + this.noise.adapt(distanceV[0]); + setMetric("Distance", distanceV[0]) + + distance.dispose(); + tfObs0.dispose(); + } + + /** + * Eval the actions and the Q function + * @param states (tf.tensor2d) + * @return actions and qValues + */ + eval(observation){ + const tfActions = this.perturbedActor.model.predict(observation); + const tfQValues = this.critic.model.predict([observation, tfActions]); + return {tfActions, tfQValues}; + } + + /** + * Get the estimation of the Q value given the state + * and the action + * @param state number[] + * @param action [a, steering] + */ + getQvalue(state, a){ + const st = tf.tensor2d([state]); + const tfa = tf.tensor2d([a]); + const q = this.critic.model.predict([st, tfa]); + const v = q.buffer().values + st.dispose(); + tfa.dispose(); + q.dispose(); + return v[0]; + } + + /** + * @param observation (tf.tensor2d) + * @return (tf.tensor1d) + */ + predict(observation){ + const tfActions = this.actor.model.predict(observation); + return tfActions; + } + + /** + * @param observation (tf.tensor2d) + * @return (tf.tensor1d) + */ + perturbedPrediction(observation){ + const tfActions = this.perturbedActor.model.predict(observation); + return tfActions; + } + + /** + * Update the two target network + */ + targetUpdate(){ + // Define in js/DDPG/models.js + //assignModel(this.critic, this.criticTarget); + //assignModel(this.actor, this.actorTarget); + targetUpdate(this.criticTarget, this.critic, this.config); + targetUpdate(this.actorTarget, this.actor, this.config); + } + + trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals){ + + const criticLoss = this.criticOptimiser.minimize(() => { + const tfQPredictions0 = this.critic.model.predict([tfObs0, tfActions]); + + const tfAPredictions = this.actorTarget.model.predict(tfObs1); + const tfQPredictions = this.criticTarget.model.predict([tfObs1, tfAPredictions]); + + const tfQTargets = tfRewards.add(tf.scalar(1).sub(tfTerminals).mul(this.tfGamma).mul(tfQPredictions)); + + const loss = tf.sub(tfQTargets, tfQPredictions0).square().mean(); + return loss; + }, true, this.criticWeights); + + setMetric("CriticLoss", criticLoss.buffer().values[0]); + criticLoss.dispose(); + } + + trainActor(tfObs0, it){ + for (let i = 0; i < it; i++){ + const actorLoss = this.actorOptimiser.minimize(() => { + const tfAPredictions0 = this.actor.model.predict(tfObs0); + const tfQPredictions0 = this.critic.model.predict([tfObs0, tfAPredictions0]); + const loss = tf.mean(tfQPredictions0).mul(tf.scalar(-1)); + return loss; + }, true, this.actorWeights); + const vLoss = actorLoss.buffer().values[0]; + setMetric("ActorLoss", actorLoss.buffer().values[0]); + actorLoss.dispose(); + } + } + + getTfBatch(){ + // Get batch + const batch = this.memory.getBatch(this.config.batchSize); + // Convert to tensors + const tfActions = tf.tensor2d(batch.actions); + const tfObs0 = tf.tensor2d(batch.obs0); + const tfObs1 = tf.tensor2d(batch.obs1); + const _tfRewards = tf.tensor1d(batch.rewards); + const _tfTerminals = tf.tensor1d(batch.terminals); + + const tfRewards = _tfRewards.expandDims(1); + const tfTerminals = _tfTerminals.expandDims(1); + + _tfRewards.dispose(); + _tfTerminals.dispose(); + + return { + tfActions, tfObs0, tfObs1, tfRewards, tfTerminals + } + } + + async optimizeCritic(){ + const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + + this.trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); + + tfActions.dispose(); + tfObs0.dispose(); + tfObs1.dispose(); + tfRewards.dispose(); + tfTerminals.dispose(); + } + + async optimizeActor(it=1){ + const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + + this.trainActor(tfObs0, it); + + tfActions.dispose(); + tfObs0.dispose(); + tfObs1.dispose(); + tfRewards.dispose(); + tfTerminals.dispose(); } -}; \ No newline at end of file +} \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/ddpg_agent.js b/demo/webapp/public/js/DDPG/ddpg_agent.js new file mode 100644 index 0000000..add5eaf --- /dev/null +++ b/demo/webapp/public/js/DDPG/ddpg_agent.js @@ -0,0 +1,168 @@ + +// This class is called from js/DDPG/index.js +class DDPGAgent { + + /** + * @param env (metacar.env) Set in js/DDPG/index.js + */ + constructor(env){ + this.stopTraining = false; + this.env = env; + // Default Config + this.config = { + "stateSize": 26, + "nbActions": 2, + "layerNom": true, + "normalizeObservations": true, + "seed": 0, + "criticL2Reg": 0.01, + "batchSize": 32, + "actorLr": 0.0001, + "criticLr": 0.001, + "gamma": 0.99, + "rewardScale": 1, + "nbEpochs": 500, + "nbEpochsCycle": 100, + "nbTrainSteps": 50, + "tau": 0.001, + "paramNoiseAdaptionInterval": 50, + }; + // From js/DDPG/noise.js + this.noise = new AdaptiveParamNoiseSpec(); + + // Configure components. + + // Buffer replay + // The baseline use 1e6 but this size should be enough for this problem + this.memoryPos = new Memory(5000); + this.memoryNeg = new Memory(5000); + // Actor and Critic are from js/DDPG/models.js + this.actor = new Actor(this.config); + this.critic = new Critic(this.config); + + // Seed javascript + Math.seedrandom(0); + + this.rewardsList = []; + + // DDPG + this.ddpg = new DDPG(this.actor, this.critic, this.memoryPos, this.memoryNeg, this.noise, this.config); + } + + /** + * Play one step + */ + play(){ + // Get the current state + const state = agent.env.getState().linear; + // Pick an action + const tfActions = agent.ddpg.predict(tf.tensor2d([state])); + const actions = tfActions.buffer().values; + agent.env.step([actions[0], actions[1]]); + tfActions.dispose(); + } + + /** + * Get the estimation of the Q value given the state + * and the action + * @param state number[] + * @param action [a, steering] + */ + getQvalue(state, a){ + return this.ddpg.getQvalue(state, a); + } + + stop(){ + this.stopTraining = true; + } + + /** + * Step into the training environement + * @param tfPreviousStep (tf.tensor2d) Current state + * @param mPreviousStep number[] + * @return {done, state} One boolean and the new state + */ + stepTrain(tfPreviousStep, mPreviousStep){ + // Get actions + const tfActions = this.ddpg.perturbedPrediction(tfPreviousStep); + // Step in the environment with theses actions + let mAcions = tfActions.buffer().values; + let mReward = this.env.step([mAcions[0], mAcions[1]]); + this.rewardsList.push(mReward); + // Get the new observations + let mState = this.env.getState().linear; + let tfState = tf.tensor2d([mState]); + let mDone = 0; + if (mReward == -1){ + mDone = 1; + } + + // Add the new tuple to the buffer + if (mReward >= 0) + this.ddpg.memoryPos.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone); + else + this.ddpg.memoryNeg.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone); + + // Dispose tensor + tfPreviousStep.dispose(); + tfActions.dispose(); + return {mDone, mState, tfState} + } + + /** + * Train DDPG Agent + */ + async train(realTime){ + this.stopTraining = false; + // One epoch + for (let e=0; e < this.config.nbEpochs; e++){ + // Perform cycles. + for (let c=0; c < this.config.nbEpochsCycle; c++){ + if (c%10==0){ + logTfMemory(); + } + this.rewardsList = []; + // Perform rollouts. + // Get current observation + let mPreviousStep = this.env.getState().linear; + let tfPreviousStep = tf.tensor2d([mPreviousStep]); + let step = 0; + console.time("LoopTime"); + for (step=0; step < 800; step++){ + let rel = this.stepTrain(tfPreviousStep, mPreviousStep); + mPreviousStep = rel.mState; + tfPreviousStep = rel.tfState; + if (rel.mDone){ + break; + } + if (this.stopTraining){ + this.env.render(true); + return; + } + if (realTime) + await tf.nextFrame(); + } + console.timeEnd("LoopTime"); + this.env.reset(); + tfPreviousStep.dispose(); + // Mean is define is js/utils.js + console.log("e="+ e +", c="+c); + setMetric("Reward", mean(this.rewardsList)); + setMetric("EpisodeDuration", step); + this.ddpg.adaptParamNoise(); + await tf.nextFrame(); + } + console.time("LoopTrain"); + for (let t=0; t < 100; t++){ + this.ddpg.optimizeCritic(); + } + for (let t=0; t < 100; t++){ + this.ddpg.optimizeActor(); + } + console.timeEnd("LoopTrain"); + this.ddpg.targetUpdate(); + } + this.env.render(true); + } + +}; \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/index.js b/demo/webapp/public/js/DDPG/index.js index 30ebda4..5ceb838 100644 --- a/demo/webapp/public/js/DDPG/index.js +++ b/demo/webapp/public/js/DDPG/index.js @@ -1,17 +1,41 @@ let levelUrl = metacar.level.level2; -// js/DDPG/ddpg.js -var ddpg = new DDPG(); - var env = new metacar.env("canvas", levelUrl); env.setAgentMotion(metacar.motion.ControlMotion, {}); + +// js/DDPG/ddpg.js +var agent = new DDPGAgent(env); + +initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "Distance"]); + env.loop(() => { let state = env.getState(); - displayState("realtime_viewer", state, 200, 200); + displayState("realtime_viewer", state.lidar, 200, 200); let reward = env.getLastReward(); - displayScores("realtime_viewer", [], reward, []); + const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); + displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]); }); -env.load(); \ No newline at end of file +env.load().then(() => { + // Train agent + env.addEvent("train", () => { + agent.train(false); + }); + + env.addEvent("play", () => { + agent.play(); + }); + + env.addEvent("TrainRealTime", () => { + env.steping(false); + agent.train(true); + }); + + env.addEvent("stop", () => { + agent.stop(); + }); + + env.addEvent("reset_env"); +}); diff --git a/demo/webapp/public/js/DDPG/memory.js b/demo/webapp/public/js/DDPG/memory.js index 6b13d58..fc8db0a 100644 --- a/demo/webapp/public/js/DDPG/memory.js +++ b/demo/webapp/public/js/DDPG/memory.js @@ -32,21 +32,24 @@ class Memory { * @return batch [] */ getBatch(batchSize){ - const arrLength = this.obs0List.length; + const arrLength = this.length; const batch = { 'obs0': [], 'obs1': [], 'rewards': [], 'actions': [], - 'terminals1': [], + 'terminals': [], }; + if (batchSize > this.length){ + return batch; + } for (let b=0; b < batchSize; b++){ let id = Math.floor(Math.random() * arrLength); batch.obs0.push(this.obs0List[id]); batch.obs1.push(this.obs1List[id]); batch.rewards.push(this.rewardsList[id]); batch.actions.push(this.actionsList[id]); - batch.terminals1.push(this.terminals1List[id]); + batch.terminals.push(this.terminals1List[id]); } return batch } @@ -63,15 +66,18 @@ class Memory { this.length += 1; } else if (this.length == this.maxlen) { + //this.obs0List[(this.start + this.length - 1) % this.maxlen].dispose(); + //this.obs1List[(this.start + this.length - 1) % this.maxlen].dispose(); + //this.actionsList[(this.start + this.length - 1) % this.maxlen].dispose(); this.start = (this.start + 1) % this.maxlen; } else { console.error("Memory.append: This should never be printed"); } this.obs0List[(this.start + this.length - 1) % this.maxlen] = obs0; - this.obs1List[(this.start + this.length - 1) % this.maxlen] = action; + this.obs1List[(this.start + this.length - 1) % this.maxlen] = obs1; this.rewardsList[(this.start + this.length - 1) % this.maxlen] = reward; - this.actionsList[(this.start + this.length - 1) % this.maxlen] = obs1; + this.actionsList[(this.start + this.length - 1) % this.maxlen] = action; this.terminals1List[(this.start + this.length - 1) % this.maxlen] = terminal1; } } \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/models.js b/demo/webapp/public/js/DDPG/models.js index 5a97256..b47a55d 100644 --- a/demo/webapp/public/js/DDPG/models.js +++ b/demo/webapp/public/js/DDPG/models.js @@ -1,17 +1,93 @@ +/** + * Copy a model + * @param model Actor|Critic instance + * @param instance Actor|Critic + * @return Copy of the model + */ +function copyModel(model, instance){ + return tf.tidy(() => { + nModel = new instance(model.config); + // action might be not required + nModel.buildModel(model.obs, model.action); + const weights = model.model.weights; + for (let m=0; m < weights.length; m++){ + nModel.model.weights[m].val.assign(weights[m].val); + } + return nModel; + }) +} + +/** + * Usefull method to copy a model + * @param model Actor + * @param perturbedActor Actor + * @param stddev (number) + * @return Copy of the model + */ +function assignAndStd(actor, perturbedActor, stddev){ + return tf.tidy(() => { + const weights = actor.model.weights; + for (let m=0; m < weights.length; m++){ + let shape = perturbedActor.model.weights[m].val.shape; + let randomTensor = tf.randomNormal(shape, 0, stddev); + let nValue = weights[m].val.add(randomTensor); + perturbedActor.model.weights[m].val.assign(nValue); + } + }); +} + +/** + * Usefull method to copy a model + * @param model Actor + * @param perturbedActor Actor + * @return Copy of the model + */ +function assignModel(model, targetModel){ + return tf.tidy(() => { + const weights = model.model.weights; + for (let m=0; m < weights.length; m++){ + let nValue = weights[m].val; + targetModel.model.weights[m].val.assign(nValue); + } + }); +} + + +/** + * Usefull method to copy a model + * @param target Actor|Critic + * @param perturbedActor Actor|Critic + * @param config (Object) + * @return Copy of the model + */ +function targetUpdate(target, original, config){ + return tf.tidy(() => { + const originalW = original.model.weights; + const targetW = target.model.weights; + + const one = tf.scalar(1); + const tau = tf.scalar(config.tau); + + for (let m=0; m < originalW.length; m++){ + let nValue = tau.mul(originalW[m].val).add(targetW[m].val.mul(one.sub(tau))); + target.model.weights[m].val.assign(nValue); + } + }); +} + class Actor{ /** - * @param stateSize(number) - * @param nbActions (number) - * @param layerNorm (boolean) - * @param seed (number) + @param config (Object) */ - constructor(stateSize, nbActions, layerNorm, seed) { - this.stateSize = stateSize; - this.nbActions = nbActions; - this.layerNorm = layerNorm; - this.seed = seed; + constructor(config) { + this.stateSize = config.stateSize; + this.nbActions = config.nbActions; + this.layerNorm = config.layerNorm; + this.seed = config.seed; + this.config = config; + this.obs = null; } /** @@ -19,14 +95,17 @@ class Actor{ * @param obs tf.input */ buildModel(obs){ + this.obs = obs; + + this.firstLayerBatchNorm = null; this.secondLayerBatchNorm = null; - this.relu = tf.layers.thresholdedReLU(); + this.relu1 = tf.layers.activation({activation: 'relu'}); + //this.relu2 = tf.layers.activation({activation: 'relu'}); // First layer with BatchNormalization this.firstLayer = tf.layers.dense({ - inputShape: this.stateSize, units: 64, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'linear', // relu is add later @@ -40,10 +119,10 @@ class Actor{ center: true }); } - + + /* // Second layer with BatchNormalization this.secondLayer = tf.layers.dense({ - inputShape: 64, units: 64, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'linear', // relu is add later @@ -56,11 +135,10 @@ class Actor{ scale: true, center: true }); - } + }*/ // Ouput layer this.outputLayer = tf.layers.dense({ - inputShape: 64, units: this.nbActions, kernelInitializer: tf.initializers.randomUniform({ minval: 0.003, maxval: 0.003, seed: this.seed}), @@ -68,41 +146,43 @@ class Actor{ useBias: true, biasInitializer: "zeros" }); - - // Actor prediction - const predict = () => { + + this.predict = () => { return tf.tidy(() => { let l1 = this.firstLayer.apply(obs); if (this.firstLayerBatchNorm){ l1 = this.firstLayerBatchNorm.apply(l1); } - //l1 = this.relu.apply(l1); + l1 = this.relu1.apply(l1); + /* let l2 = this.secondLayer.apply(l1); if (this.secondLayerBatchNorm){ l2 = this.secondLayerBatchNorm.apply(l2); } - //l2 = this.relu.apply(l2); - return this.outputLayer.apply(l2); + l2 = this.relu2.apply(l2); + */ + + return this.outputLayer.apply(l1); }); } - const output = predict(); - this.model = tf.model({inputs: this.obs, outputs: output}); + const output = this.predict(); + this.model = tf.model({inputs: obs, outputs: output}); } - }; class Critic { /** - * @param stateSize(number) - * @param nbActions (number) - * @param layerNorm (boolean) - * @param seed (number) + * @param config (Object) */ - constructor(stateSize, nbActions, layerNorm, seed) { - this.stateSize = stateSize; - this.nbActions = nbActions; - this.layerNorm = layerNorm; + constructor(config) { + this.stateSize = config.stateSize; + this.nbActions = config.nbActions; + this.layerNorm = config.layerNorm; + this.seed = config.seed; + this.config = config; + this.obs = null; + this.action = null; } /** @@ -111,14 +191,18 @@ class Critic { * @param action tf.input */ buildModel(obs, action){ + this.obs = obs; + this.action = action; + this.firstLayerBatchNorm = null; this.secondLayerBatchNorm = null; - this.relu = tf.layers.thresholdedReLU(); + this.relu1 = tf.layers.activation({activation: 'relu'}); + this.relu2 = tf.layers.activation({activation: 'relu'}); + this.concat = tf.layers.concatenate(); // First layer with BatchNormalization this.firstLayer = tf.layers.dense({ - inputShape: this.stateSize, units: 64, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'linear', // relu is add later @@ -135,7 +219,7 @@ class Critic { // Second layer with BatchNormalization this.secondLayer = tf.layers.dense({ - inputShape: 64 + this.nbActions, // Previous layer + action + //inputShape: [this.config.batchSize, 64 + this.nbActions], // Previous layer + action units: 64, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'linear', // relu is add later @@ -152,34 +236,34 @@ class Critic { // Ouput layer this.outputLayer = tf.layers.dense({ - inputShape: 64, units: 1, kernelInitializer: tf.initializers.randomUniform({ minval: 0.003, maxval: 0.003, seed: this.seed}), - activation: 'tanh', + activation: 'linear', useBias: true, biasInitializer: "zeros" }); // Actor prediction - const predict = () => { + this.predict = () => { return tf.tidy(() => { let l1 = this.firstLayer.apply(obs); - l1 = l1.concat(action); + l1 = this.concat.apply([l1, action]); if (this.firstLayerBatchNorm){ l1 = this.firstLayerBatchNorm.apply(l1); } - //l1 = this.relu(l1); + l1 = this.relu1.apply(l1); + let l2 = this.secondLayer.apply(l1); if (this.secondLayerBatchNorm){ l2 = this.secondLayerBatchNorm.apply(l2); } - //l2 = this.relu.apply(l2); + l2 = this.relu2.apply(l2); + return this.outputLayer.apply(l2); }); } - const output = predict(); - this.model = tf.model({inputs: this.obs, outputs: output}); + const output = this.predict(); + this.model = tf.model({inputs: [obs, action], outputs: output}); } - }; diff --git a/demo/webapp/public/js/DDPG/noise.js b/demo/webapp/public/js/DDPG/noise.js index c173625..c3a138f 100644 --- a/demo/webapp/public/js/DDPG/noise.js +++ b/demo/webapp/public/js/DDPG/noise.js @@ -6,7 +6,7 @@ * See "C Adapative Scaling" Page 14 in the paper. */ -class AdaptiveParamNoiseSpec{ +class AdaptiveParamNoiseSpec { /** * @param conf Object @@ -19,7 +19,7 @@ class AdaptiveParamNoiseSpec{ this.initialStddev = conf.initialStddev || 0.1; this.desiredActionStddev = conf.initialStddev || 0.1; this.adoptionCoefficient = conf.adoptionCoefficient || 1.01; - this.currentStddev = conf.initialStddev; + this.currentStddev = this.initialStddev; } /** diff --git a/demo/webapp/public/js/viewer.js b/demo/webapp/public/js/viewer.js index dba4376..6994e0a 100644 --- a/demo/webapp/public/js/viewer.js +++ b/demo/webapp/public/js/viewer.js @@ -112,4 +112,51 @@ function displayState(id, state, width, height){ yPos += ySize; } +} + +METRICS = {}; + +function initMetricsContainer(container, metrics){ + container = document.getElementById(container); + for (let m=0; m < metrics.length; m++){ + nDiv = document.createElement("div"); + nDiv.id = 'metrics_'+metrics[m]; + nDiv.style.width = "250px"; + nDiv.style.height = "250px"; + nDiv.style.display = "inline-block"; + nDiv.style.marginRight = "10px"; + container.appendChild(nDiv); + + METRICS[metrics[m]] = new CanvasJS.Chart('metrics_'+metrics[m], { + width: 250, + height: 250, + animationEnabled: false, + theme: "light2", + title:{ + text: metrics[m], + }, + axisY:{ + includeZero: false + }, + data: [{ + type: "line", + dataPoints: [{y: 0}] + }] + }); + METRICS[metrics[m]].render(); + } +} + +function setMetric(name, value){ + let chart = METRICS[name]; + let size = chart.options.data[0].dataPoints.length - 1; + + if (chart.options.data[0].dataPoints.length > 500){ + chart.options.data[0].dataPoints = chart.options.data[0].dataPoints.slice(1, size); + size = size - 1; + } + + size = chart.options.data[0].dataPoints.length - 1; + chart.options.data[0].dataPoints.push({y: value, x: chart.options.data[0].dataPoints[size].x+1}); + chart.render(); } \ No newline at end of file diff --git a/src/car.ts b/src/car.ts index 23aae93..32be0e6 100644 --- a/src/car.ts +++ b/src/car.ts @@ -11,6 +11,7 @@ import { CAR_IMG, Sprite, MAP, ROADSIZE, Container, Graphics } from "./global"; import { RoadSprite } from "./asset_manager"; +import { runInThisContext } from "vm"; var Global_carId = 0; @@ -55,6 +56,10 @@ export interface CarSprite extends PIXI.Sprite { optionalTurn?: boolean; agent?: boolean; v?: number; + a?: number; + yaw_rate?: number; + last_a?: number; + last_yaw_rate?: number; } export interface LidarChild extends PIXI.Graphics { @@ -62,6 +67,14 @@ export interface LidarChild extends PIXI.Graphics { pt?: boolean; } +export interface State { + lidar?: number[][]; + linear?: number[]; + v?: number; + a?: number; + steering?: number; +} + export class Car { public level: Level|Editor; @@ -173,6 +186,8 @@ export class Car { Method used to restore the position of the car to the original position (as set into the json file) */ + this.core.a = 0; + this.core.v = 0; this.core.mx = this.info.mx; this.core.my = this.info.my; let road = this.level.getRoad(this.core.my, this.core.mx); @@ -181,19 +196,26 @@ export class Car { } } - getState(linear:boolean = false): number[][]|number[]{ + getState(): State { /* Get the current state of the car The state is the current value of each point of the lidar. */ - if (!linear) - return this.motion.state.map(function(arr: any) { return arr.slice(); }); - else{ - let state: number[] = []; - this.motion.state.map((row: number[]) => { state = state.concat(row);}); - return state; - } + let nState: State = {}; + + let linear: number[] = []; + this.motion.state.map((row: number[]) => { linear = linear.concat(row);}); + linear.push(this.core.v); + + nState.linear = linear; + nState.lidar = this.motion.state.map(function(arr: any) { return arr.slice(); }); + + nState.v = this.core.v; + nState.a = this.core.last_a; + nState.steering = this.core.last_yaw_rate; + + return nState; } step(delta: number, action:number|number[]=null){ diff --git a/src/control_motion_engine.ts b/src/control_motion_engine.ts index 73f93cc..bb68b3b 100644 --- a/src/control_motion_engine.ts +++ b/src/control_motion_engine.ts @@ -10,6 +10,7 @@ import * as U from "./utils"; export class ControlMotionEngine extends MotionEngine { private actions: (string|number)[]; + private maxSpeed: number = 1.5; constructor(level: Level) { /* @@ -133,6 +134,9 @@ export class ControlMotionEngine extends MotionEngine { Step into the environement @delta (Float) time since the last update */ + this.car.last_a = this.car.a; + this.car.last_yaw_rate = this.car.yaw_rate; + // The car lose speed over time if (this.car.v > 0 && this.car.a == 0) this.car.v = Math.max(0, this.car.v - 0.01); @@ -140,14 +144,14 @@ export class ControlMotionEngine extends MotionEngine { this.car.v = Math.min(0, this.car.v + 0.01); if (this.car.a > 0 && this.car.v >= 0) - this.car.v = Math.min(1.5, this.car.v + this.car.a*0.01); + this.car.v = Math.min(this.maxSpeed, this.car.v + this.car.a*0.01); else if (this.car.a > 0 && this.car.v < 0){ - this.car.v = Math.min(1.5, this.car.v + this.car.a*0.03); + this.car.v = Math.min(this.maxSpeed, this.car.v + this.car.a*0.03); } if (this.car.a < 0 && this.car.v <= 0) - this.car.v = Math.max(-1.5, this.car.v + this.car.a*0.01); + this.car.v = Math.max(-this.maxSpeed, this.car.v + this.car.a*0.01); else if (this.car.a < 0 && this.car.v > 0){ - this.car.v = Math.max(-1.5, this.car.v + this.car.a*0.03); + this.car.v = Math.max(-this.maxSpeed, this.car.v + this.car.a*0.03); } if (this.car.yaw_rate == 0){ this.car.x += this.car.v * Math.cos(this.car.rotation)*delta; diff --git a/src/level.ts b/src/level.ts index d675f92..a181c8c 100644 --- a/src/level.ts +++ b/src/level.ts @@ -110,12 +110,7 @@ export class Level extends World { } setReward(agent_col: any, on_road: any, action: any){ - /* - TODO: Let's the reward define in the agent class - */ - let reward = -0.1; - if (action == 0 || this.agent.core.v == 1) - reward += 0.5; + let reward = -0.8 + this.agent.core.v / this.agent.motion.maxSpeed; if (agent_col.length > 0){ reward = -1; } @@ -133,12 +128,16 @@ export class Level extends World { } - step(delta: number, action:number|number[]=null){ + step(delta: number, action:number|number[]=null, auto: boolean = true){ /* Process one step into the environement @delta (Float) time since the last update @action: (Integer) The action to take (can be null if no action) */ + if (auto && !this.steping){ + return; + } + // Go through all cars to move each one for (var c = 0; c < this.cars.length; c++) { if (this.cars[c].lidar && !this.cars[c].core.agent) // If this car can move diff --git a/src/metacar.ts b/src/metacar.ts index 9628ff2..e3a32bc 100644 --- a/src/metacar.ts +++ b/src/metacar.ts @@ -8,7 +8,7 @@ import {UIEvent} from "./ui_event"; import * as U from "./utils"; import { BasicMotionEngine, BasicMotionOptions } from "./basic_motion_engine"; import { ControlMotionEngine } from "./control_motion_engine"; -import { LidarInfoI } from "./car"; +import { LidarInfoI, State } from "./car"; /** * @local Chooce whether to load a file from the computer. @@ -132,6 +132,14 @@ export class MetaCar { this.level.render(val); } + /** + * Choose wheter the environment should step automaticly + * @param val True or False + */ + public steping(val: boolean){ + this.level.setSteping(val); + } + /** * Usefull method to save/download a string as file. * @content The content of the file @@ -157,8 +165,8 @@ export class MetaCar { * The size of the state depends of the size of the Lidar. * @return The state as a 2D Array or 1D Array (linear:true) */ - public getState(linear:boolean = false): number[][]|number[]{ - return this.level.agent.getState(linear); + public getState(): State{ + return this.level.agent.getState(); } /** @@ -167,7 +175,7 @@ export class MetaCar { @return Reward value */ public step(action: number|number[]): number{ - return this.level.step(1, action); + return this.level.step(1, action, false); } /** diff --git a/src/ui_event.ts b/src/ui_event.ts index 929db62..0b2b6cd 100644 --- a/src/ui_event.ts +++ b/src/ui_event.ts @@ -61,6 +61,7 @@ export class UIEvent { // Listen the event button.addEventListener("click", () => { this.level.render(false); + this.level.setSteping(false); if (fc) fc(); }); } diff --git a/src/world.ts b/src/world.ts index ac9481e..0d76078 100644 --- a/src/world.ts +++ b/src/world.ts @@ -21,6 +21,7 @@ export class World { protected loop: any; // Loop method called for each render protected canvasId: string; // Id of the target canvas protected cars: Car[] = []; + protected steping: boolean = true; constructor(levelContent: LevelInfo, canvasId: string) { /* @@ -124,9 +125,11 @@ export class World { render(val: boolean){ if (val){ this.app.ticker.start(); + this.steping = true; } else{ this.app.ticker.stop(); + this.steping = false; } } @@ -144,4 +147,10 @@ export class World { return this.app.renderer.plugins.interaction.mouse.global; } + /** + * Stop stepping in the environment automaticly + */ + public setSteping(val: boolean): void{ + this.steping = val; + } } \ No newline at end of file From 9342208dc77ec98f0bc607fe98764cab4efa7bf4 Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Mon, 25 Jun 2018 09:38:11 +0100 Subject: [PATCH 3/9] Steering Angle: works --- demo/webapp/public/js/DDPG/ddpg.js | 188 +++++++---- demo/webapp/public/js/DDPG/ddpg_agent.js | 110 ++++--- demo/webapp/public/js/DDPG/index.js | 43 ++- demo/webapp/public/js/DDPG/models.js | 133 +++----- demo/webapp/public/js/DDPG/noise.js | 4 +- demo/webapp/public/js/editor.js | 2 +- src/asset_manager.ts | 7 +- src/car.ts | 2 - src/control_motion_engine.ts | 6 +- src/embedded/level/level_2.ts | 383 +++++++++++++++++------ src/level.ts | 3 +- 11 files changed, 608 insertions(+), 273 deletions(-) diff --git a/demo/webapp/public/js/DDPG/ddpg.js b/demo/webapp/public/js/DDPG/ddpg.js index 04e95f9..3655528 100644 --- a/demo/webapp/public/js/DDPG/ddpg.js +++ b/demo/webapp/public/js/DDPG/ddpg.js @@ -17,11 +17,10 @@ class DDPG { * @param memory (Memory class) * @param noise (Noise class) */ - constructor(actor, critic, memoryPos, memoryNeg, noise, config){ + constructor(actor, critic, memory, noise, config){ this.actor = actor; this.critic = critic; - this.memoryPos = memoryPos; - this.memoryNeg = memoryNeg; + this.memory = memory; this.noise = noise; this.config = config; this.tfGamma = tf.scalar(config.gamma); @@ -42,6 +41,7 @@ class DDPG { // Randomly Initialize critic network Q(s, a) this.critic.buildModel(obsInput, actionInput); + // Define in js/DDPG/models.js // Init target network Q' and μ' with the same weights this.actorTarget = copyModel(this.actor, Actor); @@ -50,35 +50,38 @@ class DDPG { this.perturbedActor = copyModel(this.actor, Actor); //this.adaptivePerturbedActor = copyModel(this.actor, Actor); + this.criticWithActor = (tfState) => { + return tf.tidy(() => { + const tfAct = this.actor.predict(tfState); + return this.critic.predict(tfState, tfAct); + }); + }; + + this.criticTargetWithActorTarget = (tfState) => { + return tf.tidy(() => { + const tfAct = this.actorTarget.predict(tfState); + return this.criticTarget.predict(tfState, tfAct); + }); + }; + + this.actorOptimiser = tf.train.adam(this.config.actorLr); this.criticOptimiser = tf.train.adam(this.config.criticLr); this.criticWeights = []; - for (let w = 0; w < this.critic.model.weights.length; w++){ - this.criticWeights.push(this.critic.model.weights[w].val); + for (let w = 0; w < this.critic.model.trainableWeights.length; w++){ + this.criticWeights.push(this.critic.model.trainableWeights[w].val); } this.actorWeights = []; - for (let w = 0; w < this.actor.model.weights.length; w++){ - this.actorWeights.push(this.actor.model.weights[w].val); + for (let w = 0; w < this.actor.model.trainableWeights.length; w++){ + this.actorWeights.push(this.actor.model.trainableWeights[w].val); } - // Return a batch from positive and negative experiences - this.memory = { - getBatch: (size) => { - let batch1 = this.memoryPos.getBatch(size/2); - let batch2 = this.memoryNeg.getBatch(size/2); - return { - 'obs0': batch1.obs0.concat(batch2.obs0), - 'obs1': batch1.obs1.concat(batch2.obs1), - 'rewards': batch1.rewards.concat(batch2.rewards), - 'actions': batch1.actions.concat(batch2.actions), - 'terminals': batch1.terminals.concat(batch2.terminals), - }; - } - }; + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev, this.config.seed); - assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev); + this.trainActorCt = 0; + this.trainCriticCt = 0; } @@ -102,17 +105,22 @@ class DDPG { */ adaptParamNoise(){ const batch = this.memory.getBatch(this.config.batchSize); - const tfObs0 = tf.tensor2d(batch.obs0); - const distance = this.distanceMeasure(tfObs0); + let distanceV = null; - assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev); + if (batch.obs0.length > 0){ + const tfObs0 = tf.tensor2d(batch.obs0); + const distance = this.distanceMeasure(tfObs0); + + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev, this.config.seed); + + distanceV = distance.buffer().values; + this.noise.adapt(distanceV[0]); - const distanceV = distance.buffer().values; - this.noise.adapt(distanceV[0]); - setMetric("Distance", distanceV[0]) + distance.dispose(); + tfObs0.dispose(); + } - distance.dispose(); - tfObs0.dispose(); + return distanceV; } /** @@ -176,32 +184,59 @@ class DDPG { const criticLoss = this.criticOptimiser.minimize(() => { const tfQPredictions0 = this.critic.model.predict([tfObs0, tfActions]); - - const tfAPredictions = this.actorTarget.model.predict(tfObs1); - const tfQPredictions = this.criticTarget.model.predict([tfObs1, tfAPredictions]); - - const tfQTargets = tfRewards.add(tf.scalar(1).sub(tfTerminals).mul(this.tfGamma).mul(tfQPredictions)); + const tfQPredictions1 = this.criticTargetWithActorTarget(tfObs1); + + const tfQTargets = tfRewards.add(tf.scalar(1).sub(tfTerminals).mul(this.tfGamma).mul(tfQPredictions1)); - const loss = tf.sub(tfQTargets, tfQPredictions0).square().mean(); - return loss; + return tf.sub(tfQTargets, tfQPredictions0).square().mean(); }, true, this.criticWeights); - setMetric("CriticLoss", criticLoss.buffer().values[0]); + const loss = criticLoss.buffer().values[0]; criticLoss.dispose(); + + //if (this.trainCriticCt % 200 == 0 && this.trainCriticCt != 0){ + targetUpdate(this.criticTarget, this.critic, this.config); + //console.log("Update"); + // Saniity Check + //} + this.trainCriticCt += 1; + + return loss; } - trainActor(tfObs0, it){ - for (let i = 0; i < it; i++){ - const actorLoss = this.actorOptimiser.minimize(() => { - const tfAPredictions0 = this.actor.model.predict(tfObs0); - const tfQPredictions0 = this.critic.model.predict([tfObs0, tfAPredictions0]); - const loss = tf.mean(tfQPredictions0).mul(tf.scalar(-1)); - return loss; - }, true, this.actorWeights); - const vLoss = actorLoss.buffer().values[0]; - setMetric("ActorLoss", actorLoss.buffer().values[0]); - actorLoss.dispose(); + trainActor(tfObs0){ + + const actorLoss = this.actorOptimiser.minimize(() => { + const tfQPredictions0 = this.criticWithActor(tfObs0); + return tf.mean(tfQPredictions0).mul(tf.scalar(-1.)) + }, true, this.actorWeights); + + /* + const sanityTfLoss = tf.tidy(() => { + const tfQPredictions0 = this.criticTargetWithActor(tfObs0); + const mn = tf.mean(tfQPredictions0); + const loss = mn.mul(tf.scalar(-1)); + return loss; + }); + + const sanityLoss = sanityTfLoss.buffer().values[0]; + + + if (sanityLoss == loss){ + console.warn("Sanity check failed. The optimisation have no effet here."); } + */ + + //if (this.trainActorCt % 200 == 0 && this.trainActorCt != 0){ + targetUpdate(this.actorTarget, this.actor, this.config); + //} + //this.trainActorCt += 1; + + const loss = actorLoss.buffer().values[0]; + actorLoss.dispose(); + //sanityTfLoss.dispose(); + + return loss; } getTfBatch(){ @@ -225,27 +260,74 @@ class DDPG { } } - async optimizeCritic(){ + optimizeCritic(){ const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); - this.trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); + const loss = this.trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); tfActions.dispose(); tfObs0.dispose(); tfObs1.dispose(); tfRewards.dispose(); tfTerminals.dispose(); + + return loss; } - async optimizeActor(it=1){ + optimizeActor(it=1){ const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); - this.trainActor(tfObs0, it); + const loss = this.trainActor(tfObs0, it); tfActions.dispose(); tfObs0.dispose(); tfObs1.dispose(); tfRewards.dispose(); tfTerminals.dispose(); + + return loss; } + + optimizeCriticActor(){ + const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + + const lossC = this.trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); + const lossA = this.trainActor(tfObs0); + + tfActions.dispose(); + tfObs0.dispose(); + tfObs1.dispose(); + tfRewards.dispose(); + tfTerminals.dispose(); + + return {lossC, lossA}; + } + + trainRecord(){ + + let lossValues = []; + + for (let i=0; i < 32; i++){ + + const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + + const actorLoss = this.actorOptimiser.minimize(() => { + const tfAPredictions0 = this.actor.model.predict(tfObs0); + const loss = tfActions.sub(tfAPredictions0).square().mean(); + return loss; + }, true, this.actorWeights); + + const loss = actorLoss.buffer().values[0]; + lossValues.push(loss); + + actorLoss.dispose(); + tfActions.dispose(); + tfObs0.dispose(); + tfObs1.dispose(); + tfRewards.dispose(); + tfTerminals.dispose(); + } + console.log("Mean loss", mean(lossValues)); + } + } \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/ddpg_agent.js b/demo/webapp/public/js/DDPG/ddpg_agent.js index add5eaf..c6a3e51 100644 --- a/demo/webapp/public/js/DDPG/ddpg_agent.js +++ b/demo/webapp/public/js/DDPG/ddpg_agent.js @@ -10,21 +10,23 @@ class DDPGAgent { this.env = env; // Default Config this.config = { - "stateSize": 26, - "nbActions": 2, - "layerNom": true, + "stateSize": 17, + "nbActions": 1, + "layerNorm": false, "normalizeObservations": true, "seed": 0, "criticL2Reg": 0.01, - "batchSize": 32, + "batchSize": 64, "actorLr": 0.0001, "criticLr": 0.001, + "memorySize": 15000, "gamma": 0.99, + "noiseDecay": 0.95, "rewardScale": 1, "nbEpochs": 500, - "nbEpochsCycle": 100, + "nbEpochsCycle": 20, "nbTrainSteps": 50, - "tau": 0.001, + "tau": 0.01, "paramNoiseAdaptionInterval": 50, }; // From js/DDPG/noise.js @@ -34,8 +36,7 @@ class DDPGAgent { // Buffer replay // The baseline use 1e6 but this size should be enough for this problem - this.memoryPos = new Memory(5000); - this.memoryNeg = new Memory(5000); + this.memory = new Memory(this.config.memorySize); // Actor and Critic are from js/DDPG/models.js this.actor = new Actor(this.config); this.critic = new Critic(this.config); @@ -44,9 +45,10 @@ class DDPGAgent { Math.seedrandom(0); this.rewardsList = []; + this.epiDuration = []; // DDPG - this.ddpg = new DDPG(this.actor, this.critic, this.memoryPos, this.memoryNeg, this.noise, this.config); + this.ddpg = new DDPG(this.actor, this.critic, this.memory, this.noise, this.config); } /** @@ -54,11 +56,11 @@ class DDPGAgent { */ play(){ // Get the current state - const state = agent.env.getState().linear; + const state = this.env.getState().linear; // Pick an action - const tfActions = agent.ddpg.predict(tf.tensor2d([state])); + const tfActions = this.ddpg.predict(tf.tensor2d([state])); const actions = tfActions.buffer().values; - agent.env.step([actions[0], actions[1]]); + agent.env.step([1., actions[0]]); tfActions.dispose(); } @@ -85,48 +87,66 @@ class DDPGAgent { stepTrain(tfPreviousStep, mPreviousStep){ // Get actions const tfActions = this.ddpg.perturbedPrediction(tfPreviousStep); + //const TruetfActions = this.ddpg.perturbedPrediction(tfPreviousStep); + //const rdNormal = tf.randomNormal(TruetfActions.shape, 0, this.noisyActions, "float32", this.config.seed); + //const noisyActions = TruetfActions.add(rdNormal); + //const tfActions = tf.clipByValue(noisyActions, -1, 1); + // Step in the environment with theses actions let mAcions = tfActions.buffer().values; - let mReward = this.env.step([mAcions[0], mAcions[1]]); + let mReward = this.env.step([1., mAcions[0]]); this.rewardsList.push(mReward); // Get the new observations let mState = this.env.getState().linear; let tfState = tf.tensor2d([mState]); let mDone = 0; + if (mReward == -1){ mDone = 1; } // Add the new tuple to the buffer - if (mReward >= 0) - this.ddpg.memoryPos.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone); - else - this.ddpg.memoryNeg.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone); + this.ddpg.memory.append(mPreviousStep, [mAcions[0]], mReward, mState, mDone); // Dispose tensor tfPreviousStep.dispose(); + //TruetfActions.dispose(); + //noisyActions.dispose(); tfActions.dispose(); + //rdNormal.dispose(); + //rd.dispose(); + //trueTfActions.dispose(); return {mDone, mState, tfState} } + initTrainParam(){ + this.stopTraining = false; + this.noisyActions = 2.0; + } + /** * Train DDPG Agent */ async train(realTime){ - this.stopTraining = false; + this.initTrainParam(); // One epoch for (let e=0; e < this.config.nbEpochs; e++){ // Perform cycles. + this.rewardsList = []; + this.stepList = []; + this.distanceList = []; for (let c=0; c < this.config.nbEpochsCycle; c++){ + if (c%10==0){ logTfMemory(); } - this.rewardsList = []; + // Perform rollouts. // Get current observation let mPreviousStep = this.env.getState().linear; let tfPreviousStep = tf.tensor2d([mPreviousStep]); let step = 0; + console.time("LoopTime"); for (step=0; step < 800; step++){ let rel = this.stepTrain(tfPreviousStep, mPreviousStep); @@ -139,30 +159,48 @@ class DDPGAgent { this.env.render(true); return; } - if (realTime) + if (realTime && step % 10 == 0) await tf.nextFrame(); } + this.stepList.push(step); console.timeEnd("LoopTime"); - this.env.reset(); + let distance = this.ddpg.adaptParamNoise(); + this.distanceList.push(distance[0]); + + this.env.randomRoadPosition(); tfPreviousStep.dispose(); - // Mean is define is js/utils.js console.log("e="+ e +", c="+c); - setMetric("Reward", mean(this.rewardsList)); - setMetric("EpisodeDuration", step); - this.ddpg.adaptParamNoise(); + + //this.ddpg.targetUpdate(); await tf.nextFrame(); } - console.time("LoopTrain"); - for (let t=0; t < 100; t++){ - this.ddpg.optimizeCritic(); - } - for (let t=0; t < 100; t++){ - this.ddpg.optimizeActor(); - } - console.timeEnd("LoopTrain"); - this.ddpg.targetUpdate(); + if (this.ddpg.memory.length == this.config.memorySize){ + this.noisyActions = Math.max(0.1, this.noisyActions * this.config.noiseDecay); + this.ddpg.noise.desiredActionStddev = Math.min(0.5, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev); + let lossValuesCritic = []; + let lossValuesActor = []; + console.time("Training"); + for (let t=0; t < 100; t++){ + let lossC = this.ddpg.optimizeCritic(); + lossValuesCritic.push(lossC); + } + for (let t=0; t < 100; t++){ + let lossA = this.ddpg.optimizeActor(); + lossValuesActor.push(lossA); + } + console.timeEnd("Training"); + console.log("desiredActionStddev:", this.ddpg.noise.desiredActionStddev); + setMetric("CriticLoss", mean(lossValuesCritic)); + setMetric("ActorLoss", mean(lossValuesActor)); + } + setMetric("Reward", mean(this.rewardsList)); + setMetric("EpisodeDuration", mean(this.stepList)); + setMetric("Distance", mean(this.distanceList)); + await tf.nextFrame(); + } + + + this.env.render(true); } - this.env.render(true); - } }; \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/index.js b/demo/webapp/public/js/DDPG/index.js index 5ceb838..2810b85 100644 --- a/demo/webapp/public/js/DDPG/index.js +++ b/demo/webapp/public/js/DDPG/index.js @@ -3,8 +3,11 @@ let levelUrl = metacar.level.level2; var env = new metacar.env("canvas", levelUrl); env.setAgentMotion(metacar.motion.ControlMotion, {}); +env.setAgentLidar({pts: 4, width: 2, height: 9, pos: 1}) +RECORD = false; + // js/DDPG/ddpg.js var agent = new DDPGAgent(env); @@ -12,13 +15,21 @@ initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "Epi env.loop(() => { let state = env.getState(); + + if (RECORD){ + agent.ddpg.memory.append(state.linear, [state.a, state.steering], 1, [1, 1, 1], 1); + } + displayState("realtime_viewer", state.lidar, 200, 200); let reward = env.getLastReward(); - const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); + const qValue = agent.getQvalue(state.linear, [state.steering]); displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]); }); + env.load().then(() => { + + // Train agent env.addEvent("train", () => { agent.train(false); @@ -28,6 +39,22 @@ env.load().then(() => { agent.play(); }); + env.addEvent("record", () => { + RECORD = true; + }); + + env.addEvent("randomPos", () => { + env.randomRoadPosition(); + }); + + env.addEvent("stopRecord", () => { + RECORD = false; + }); + + env.addEvent("trainOnRecord", () => { + agent.ddpg.trainRecord(); + }); + env.addEvent("TrainRealTime", () => { env.steping(false); agent.train(true); @@ -38,4 +65,18 @@ env.load().then(() => { }); env.addEvent("reset_env"); + + env.addEvent("load", (content) => { + content = JSON.parse(content); + + for (let c=0; c < content.obs0.length; c++){ + if (content.obs0[c] != 0){ + agent.ddpg.memory.append(content.obs0[c], content.actions[c], content.rewards[c], content.obs0[c], content.terminals[c]); + } + } + + console.log("dataReady"); + + }, {local: true}); }); + diff --git a/demo/webapp/public/js/DDPG/models.js b/demo/webapp/public/js/DDPG/models.js index b47a55d..65c0edb 100644 --- a/demo/webapp/public/js/DDPG/models.js +++ b/demo/webapp/public/js/DDPG/models.js @@ -24,14 +24,14 @@ function copyModel(model, instance){ * @param stddev (number) * @return Copy of the model */ -function assignAndStd(actor, perturbedActor, stddev){ +function assignAndStd(actor, perturbedActor, stddev, seed){ return tf.tidy(() => { - const weights = actor.model.weights; + const weights = actor.model.trainableWeights; for (let m=0; m < weights.length; m++){ - let shape = perturbedActor.model.weights[m].val.shape; - let randomTensor = tf.randomNormal(shape, 0, stddev); + let shape = perturbedActor.model.trainableWeights[m].val.shape; + let randomTensor = tf.randomNormal(shape, 0, stddev, "float32", seed); let nValue = weights[m].val.add(randomTensor); - perturbedActor.model.weights[m].val.assign(nValue); + perturbedActor.model.trainableWeights[m].val.assign(nValue); } }); } @@ -44,10 +44,10 @@ function assignAndStd(actor, perturbedActor, stddev){ */ function assignModel(model, targetModel){ return tf.tidy(() => { - const weights = model.model.weights; + const weights = model.model.trainableWeights; for (let m=0; m < weights.length; m++){ let nValue = weights[m].val; - targetModel.model.weights[m].val.assign(nValue); + targetModel.model.trainableWeights[m].val.assign(nValue); } }); } @@ -62,15 +62,20 @@ function assignModel(model, targetModel){ */ function targetUpdate(target, original, config){ return tf.tidy(() => { - const originalW = original.model.weights; - const targetW = target.model.weights; + const originalW = original.model.trainableWeights; + const targetW = target.model.trainableWeights; const one = tf.scalar(1); const tau = tf.scalar(config.tau); for (let m=0; m < originalW.length; m++){ + const lastValue = target.model.trainableWeights[m].val.clone(); let nValue = tau.mul(originalW[m].val).add(targetW[m].val.mul(one.sub(tau))); - target.model.weights[m].val.assign(nValue); + target.model.trainableWeights[m].val.assign(nValue); + const diff = lastValue.sub(target.model.trainableWeights[m].val).mean().buffer().values; + if (diff[0] == 0){ + console.warn("targetUpdate: Nothing have been changed!") + } } }); } @@ -97,45 +102,23 @@ class Actor{ buildModel(obs){ this.obs = obs; - - this.firstLayerBatchNorm = null; - this.secondLayerBatchNorm = null; - - this.relu1 = tf.layers.activation({activation: 'relu'}); - //this.relu2 = tf.layers.activation({activation: 'relu'}); - // First layer with BatchNormalization this.firstLayer = tf.layers.dense({ units: 64, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), - activation: 'linear', // relu is add later + activation: 'relu', // relu is add later useBias: true, biasInitializer: "zeros" }); - if (this.layerNorm){ - // WARNING: BatchNormalization instead of layerNormalization - this.firstLayerBatchNorm = tf.layers.batchNormalization({ - scale: true, - center: true - }); - } - - /* + // Second layer with BatchNormalization this.secondLayer = tf.layers.dense({ - units: 64, + units: 32, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), - activation: 'linear', // relu is add later + activation: 'relu', // relu is add later useBias: true, biasInitializer: "zeros" }); - if (this.layerNorm){ - // WARNING: BatchNormalization instead of layerNormalization - this.secondLayerBatchNorm = tf.layers.batchNormalization({ - scale: true, - center: true - }); - }*/ // Ouput layer this.outputLayer = tf.layers.dense({ @@ -147,22 +130,15 @@ class Actor{ biasInitializer: "zeros" }); - this.predict = () => { - return tf.tidy(() => { - let l1 = this.firstLayer.apply(obs); - if (this.firstLayerBatchNorm){ - l1 = this.firstLayerBatchNorm.apply(l1); + this.predict = (tfState) => { + return tf.tidy(() => { + if (tfState){ + obs = tfState; } - l1 = this.relu1.apply(l1); - /* + let l1 = this.firstLayer.apply(obs); let l2 = this.secondLayer.apply(l1); - if (this.secondLayerBatchNorm){ - l2 = this.secondLayerBatchNorm.apply(l2); - } - l2 = this.relu2.apply(l2); - */ - - return this.outputLayer.apply(l1); + + return this.outputLayer.apply(l2); }); } const output = this.predict(); @@ -194,45 +170,35 @@ class Critic { this.obs = obs; this.action = action; - this.firstLayerBatchNorm = null; - this.secondLayerBatchNorm = null; + this.add = tf.layers.add(); - this.relu1 = tf.layers.activation({activation: 'relu'}); - this.relu2 = tf.layers.activation({activation: 'relu'}); - this.concat = tf.layers.concatenate(); + // First layer with BatchNormalization + this.firstLayerS = tf.layers.dense({ + units: 64, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'linear', // relu is add later + useBias: true, + biasInitializer: "zeros" + }); // First layer with BatchNormalization - this.firstLayer = tf.layers.dense({ + this.firstLayerA = tf.layers.dense({ units: 64, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'linear', // relu is add later useBias: true, biasInitializer: "zeros" }); - if (this.layerNorm){ - // WARNING: BatchNormalization instead of layerNormalization - this.firstLayerBatchNorm = tf.layers.batchNormalization({ - scale: true, - center: true - }); - } // Second layer with BatchNormalization this.secondLayer = tf.layers.dense({ //inputShape: [this.config.batchSize, 64 + this.nbActions], // Previous layer + action - units: 64, + units: 32, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), - activation: 'linear', // relu is add later + activation: 'relu', useBias: true, biasInitializer: "zeros" }); - if (this.layerNorm){ - // WARNING: BatchNormalization instead of layerNormalization - this.secondLayerBatchNorm = tf.layers.batchNormalization({ - scale: true, - center: true - }); - } // Ouput layer this.outputLayer = tf.layers.dense({ @@ -245,22 +211,21 @@ class Critic { }); // Actor prediction - this.predict = () => { + this.predict = (tfState, tfActions) => { return tf.tidy(() => { - let l1 = this.firstLayer.apply(obs); - l1 = this.concat.apply([l1, action]); - if (this.firstLayerBatchNorm){ - l1 = this.firstLayerBatchNorm.apply(l1); + if (tfState && tfActions){ + obs = tfState; + action = tfActions; } - l1 = this.relu1.apply(l1); + + let l1A = this.firstLayerA.apply(action); + let l1S = this.firstLayerS.apply(obs) - let l2 = this.secondLayer.apply(l1); - if (this.secondLayerBatchNorm){ - l2 = this.secondLayerBatchNorm.apply(l2); - } - l2 = this.relu2.apply(l2); + let concat = this.add.apply([l1A, l1S]) - return this.outputLayer.apply(l2); + let l2 = this.secondLayer.apply(concat); + + return this.outputLayer.apply(l2); }); } const output = this.predict(); diff --git a/demo/webapp/public/js/DDPG/noise.js b/demo/webapp/public/js/DDPG/noise.js index c3a138f..9c0ebb2 100644 --- a/demo/webapp/public/js/DDPG/noise.js +++ b/demo/webapp/public/js/DDPG/noise.js @@ -16,8 +16,8 @@ class AdaptiveParamNoiseSpec { */ constructor(conf){ conf = conf || {}; - this.initialStddev = conf.initialStddev || 0.1; - this.desiredActionStddev = conf.initialStddev || 0.1; + this.initialStddev = conf.initialStddev || 0.3; + this.desiredActionStddev = conf.desiredActionStddev || 0.3; this.adoptionCoefficient = conf.adoptionCoefficient || 1.01; this.currentStddev = this.initialStddev; } diff --git a/demo/webapp/public/js/editor.js b/demo/webapp/public/js/editor.js index 97b0e54..3a569b3 100644 --- a/demo/webapp/public/js/editor.js +++ b/demo/webapp/public/js/editor.js @@ -16,5 +16,5 @@ editor.load().then(() => { localStorage.setItem('mylevel.json', JSON.stringify(content)); window.open("/test_editor.html"); - }, {download: false, name: "level.json"}); + }, {download: true, name: "level.json"}); }); \ No newline at end of file diff --git a/src/asset_manager.ts b/src/asset_manager.ts index 8a3c97c..c93474d 100644 --- a/src/asset_manager.ts +++ b/src/asset_manager.ts @@ -140,6 +140,9 @@ export class AssetManger { } car.line = line; + car.a = 0; + car.v = 0; + // Set the car on the road car.x = road.x; car.y = road.y; @@ -158,7 +161,6 @@ export class AssetManger { let line_side_factor_t = line == 0 ? 0:Math.PI; car.x += line_side_factor*Math.round(x_m); car.y += line_side_factor*Math.round(y_m); - // (x, y position) Relatif to the map car.mx = Math.floor(car.x / ROADSIZE); car.my = Math.floor(car.y / ROADSIZE); @@ -166,6 +168,9 @@ export class AssetManger { car.rotation = (th+line_side_factor_t); // Set the new road of the car car.checkAndsetNewRoad(road); + + + } createAsset(img: string, info: AssetInfo, textures: any, type: string){ diff --git a/src/car.ts b/src/car.ts index 32be0e6..a4a6309 100644 --- a/src/car.ts +++ b/src/car.ts @@ -186,8 +186,6 @@ export class Car { Method used to restore the position of the car to the original position (as set into the json file) */ - this.core.a = 0; - this.core.v = 0; this.core.mx = this.info.mx; this.core.my = this.info.my; let road = this.level.getRoad(this.core.my, this.core.mx); diff --git a/src/control_motion_engine.ts b/src/control_motion_engine.ts index bb68b3b..437269c 100644 --- a/src/control_motion_engine.ts +++ b/src/control_motion_engine.ts @@ -10,7 +10,7 @@ import * as U from "./utils"; export class ControlMotionEngine extends MotionEngine { private actions: (string|number)[]; - private maxSpeed: number = 1.5; + private maxSpeed: number = 2.0; constructor(level: Level) { /* @@ -144,12 +144,12 @@ export class ControlMotionEngine extends MotionEngine { this.car.v = Math.min(0, this.car.v + 0.01); if (this.car.a > 0 && this.car.v >= 0) - this.car.v = Math.min(this.maxSpeed, this.car.v + this.car.a*0.01); + this.car.v = Math.min(this.maxSpeed, this.car.v + this.car.a*0.005); else if (this.car.a > 0 && this.car.v < 0){ this.car.v = Math.min(this.maxSpeed, this.car.v + this.car.a*0.03); } if (this.car.a < 0 && this.car.v <= 0) - this.car.v = Math.max(-this.maxSpeed, this.car.v + this.car.a*0.01); + this.car.v = Math.max(-this.maxSpeed, this.car.v + this.car.a*0.005); else if (this.car.a < 0 && this.car.v > 0){ this.car.v = Math.max(-this.maxSpeed, this.car.v + this.car.a*0.03); } diff --git a/src/embedded/level/level_2.ts b/src/embedded/level/level_2.ts index 49a32f8..88a98ea 100644 --- a/src/embedded/level/level_2.ts +++ b/src/embedded/level/level_2.ts @@ -1,114 +1,319 @@ export const level2: any = { - "cars": [ + "cars": [], + "road": [ { - "mx": 7, - "my": 7, - "line": 0 + "x": 300, + "y": 360 }, { - "mx": 9, - "my": 6, - "line": 0 + "x": 300, + "y": 360 }, { - "mx": 8, - "my": 6, - "line": 1 + "x": 360, + "y": 360 }, { - "mx": 10, - "my": 6, - "line": 1 - } - ], - "house": [ + "x": 420, + "y": 360 + }, + { + "x": 240, + "y": 360 + }, + { + "x": 180, + "y": 360 + }, + { + "x": 120, + "y": 360 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 120, + "y": 360 + }, + { + "x": 60, + "y": 360 + }, + { + "x": 300, + "y": 300 + }, + { + "x": 300, + "y": 240 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 540, + "y": 360 + }, + { + "x": 300, + "y": 180 + }, + { + "x": 360, + "y": 180 + }, + { + "x": 240, + "y": 180 + }, + { + "x": 240, + "y": 180 + }, + { + "x": 180, + "y": 180 + }, + { + "x": 420, + "y": 180 + }, + { + "x": 420, + "y": 120 + }, + { + "x": 180, + "y": 120 + }, + { + "x": 300, + "y": 60 + }, + { + "x": 420, + "y": 60 + }, + { + "x": 180, + "y": 60 + }, + { + "x": 240, + "y": 60 + }, + { + "x": 360, + "y": 60 + }, { - "x": 68, - "y": 317 + "x": 480, + "y": 60 + }, + { + "x": 120, + "y": 60 + }, + { + "x": 540, + "y": 60 + }, + { + "x": 540, + "y": 120 + }, + { + "x": 540, + "y": 180 + }, + { + "x": 540, + "y": 240 + }, + { + "x": 540, + "y": 300 + }, + { + "x": 60, + "y": 60 + }, + { + "x": 60, + "y": 120 + }, + { + "x": 60, + "y": 180 + }, + { + "x": 60, + "y": 240 + }, + { + "x": 60, + "y": 300 + }, + { + "x": 60, + "y": 240 + }, + { + "x": 360, + "y": 180 + }, + { + "x": 480, + "y": 360 } ], - "house2": [ + "asset": [ { - "x": 624, - "y": 84 + "x": 162, + "y": 264 }, { - "x": -15, - "y": 144 + "x": 407, + "y": 264 + }, + { + "x": 259, + "y": 126 + }, + { + "x": 306, + "y": 33 + }, + { + "x": 491, + "y": 148 + }, + { + "x": 391, + "y": 431 + }, + { + "x": 608, + "y": 35 + }, + { + "x": 484, + "y": 224 + }, + { + "x": 248, + "y": 297 + }, + { + "x": 376, + "y": 336 + }, + { + "x": 78, + "y": 429 + }, + { + "x": 1, + "y": 205 + }, + { + "x": 3, + "y": 106 + }, + { + "x": -46, + "y": 290 + }, + { + "x": 622, + "y": 154 + }, + { + "x": 617, + "y": 280 } ], - "house3": [ + "house": [ { - "x": 618, - "y": 231 + "x": 162, + "y": 264 }, { - "x": 13, - "y": 441 + "x": 622, + "y": 154 } ], - "bench": [ + "house3": [ { - "x": 318, - "y": 216 + "x": 407, + "y": 264 }, { - "x": 422, - "y": 36 + "x": -46, + "y": 290 } ], "tree": [ { - "x": 151, - "y": 144 + "x": 259, + "y": 126 }, { - "x": 229, - "y": 175 + "x": 491, + "y": 148 }, { - "x": 326, - "y": 132 + "x": 391, + "y": 431 }, { - "x": 413, - "y": 167 + "x": 608, + "y": 35 }, { - "x": 487, - "y": 157 + "x": 484, + "y": 224 }, { - "x": 445, - "y": 123 + "x": 248, + "y": 297 }, { - "x": 511, + "x": 78, "y": 429 }, { - "x": 584, - "y": 427 - }, - { - "x": 80, - "y": 391 + "x": 1, + "y": 205 }, { - "x": 219, - "y": 409 + "x": 3, + "y": 106 }, { - "x": 207, - "y": 326 - }, + "x": 617, + "y": 280 + } + ], + "bench": [ { - "x": 156, - "y": 426 + "x": 306, + "y": 33 }, { - "x": 162, - "y": 13 + "x": 376, + "y": 336 } ], "map": [ @@ -129,11 +334,11 @@ export const level2: any = { 0, "↱", "↔", + "↧", "↔", "↔", "↔", - "↔", - "↔", + "↧", "↔", "↰", 0 @@ -142,11 +347,11 @@ export const level2: any = { 0, "↕", 0, + "↕", 0, 0, 0, - 0, - 0, + "↕", 0, "↕", 0 @@ -154,32 +359,19 @@ export const level2: any = { [ 0, "↕", - 0, - 0, - 0, - 0, - 0, - 0, - 0, - "↕", - 0 - ], - [ 0, "↳", "↔", - "↔", - "↔", "↧", - "↔", - "↔", - "↔", + "↠", "↲", + 0, + "↕", 0 ], [ 0, - 0, + "↟", 0, 0, 0, @@ -187,21 +379,34 @@ export const level2: any = { 0, 0, 0, - 0, + "↕", 0 ], [ + 0, + "↕", 0, 0, 0, + "↕", + 0, 0, 0, "↕", + 0 + ], + [ 0, - "↱", + "↳", + "↔", + "↔", "↔", + "↥", "↔", - "↔" + "↔", + "↠", + "↲", + 0 ], [ 0, @@ -209,17 +414,17 @@ export const level2: any = { 0, 0, 0, - "↕", 0, - "↕", + 0, + 0, 0, 0, 0 ] ], "agent": { - "mx": 2, - "my": 4, + "mx": 4, + "my": 3, "line": 0, "motion": { "type": "BasicMotionEngine", diff --git a/src/level.ts b/src/level.ts index a181c8c..605640c 100644 --- a/src/level.ts +++ b/src/level.ts @@ -110,7 +110,8 @@ export class Level extends World { } setReward(agent_col: any, on_road: any, action: any){ - let reward = -0.8 + this.agent.core.v / this.agent.motion.maxSpeed; + let reward = 0; + //let reward = -0.8 + this.agent.core.v / this.agent.motion.maxSpeed; if (agent_col.length > 0){ reward = -1; } From ea344a6cb74f80372dc3d40512d547787965f9bf Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Mon, 25 Jun 2018 11:18:13 +0100 Subject: [PATCH 4/9] Steering angle and throttle together: works --- demo/webapp/public/js/DDPG/ddpg_agent.js | 14 +++++++------- demo/webapp/public/js/DDPG/index.js | 2 +- demo/webapp/public/js/DDPG/noise.js | 4 ++-- src/level.ts | 3 +-- 4 files changed, 11 insertions(+), 12 deletions(-) diff --git a/demo/webapp/public/js/DDPG/ddpg_agent.js b/demo/webapp/public/js/DDPG/ddpg_agent.js index c6a3e51..553575b 100644 --- a/demo/webapp/public/js/DDPG/ddpg_agent.js +++ b/demo/webapp/public/js/DDPG/ddpg_agent.js @@ -11,7 +11,7 @@ class DDPGAgent { // Default Config this.config = { "stateSize": 17, - "nbActions": 1, + "nbActions": 2, "layerNorm": false, "normalizeObservations": true, "seed": 0, @@ -19,9 +19,9 @@ class DDPGAgent { "batchSize": 64, "actorLr": 0.0001, "criticLr": 0.001, - "memorySize": 15000, + "memorySize": 20000, "gamma": 0.99, - "noiseDecay": 0.95, + "noiseDecay": 0.99, "rewardScale": 1, "nbEpochs": 500, "nbEpochsCycle": 20, @@ -60,7 +60,7 @@ class DDPGAgent { // Pick an action const tfActions = this.ddpg.predict(tf.tensor2d([state])); const actions = tfActions.buffer().values; - agent.env.step([1., actions[0]]); + agent.env.step([actions[0], actions[1]]); tfActions.dispose(); } @@ -94,7 +94,7 @@ class DDPGAgent { // Step in the environment with theses actions let mAcions = tfActions.buffer().values; - let mReward = this.env.step([1., mAcions[0]]); + let mReward = this.env.step([mAcions[0], mAcions[1]]); this.rewardsList.push(mReward); // Get the new observations let mState = this.env.getState().linear; @@ -106,7 +106,7 @@ class DDPGAgent { } // Add the new tuple to the buffer - this.ddpg.memory.append(mPreviousStep, [mAcions[0]], mReward, mState, mDone); + this.ddpg.memory.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone); // Dispose tensor tfPreviousStep.dispose(); @@ -176,7 +176,7 @@ class DDPGAgent { } if (this.ddpg.memory.length == this.config.memorySize){ this.noisyActions = Math.max(0.1, this.noisyActions * this.config.noiseDecay); - this.ddpg.noise.desiredActionStddev = Math.min(0.5, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev); + this.ddpg.noise.desiredActionStddev = Math.max(0.1, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev); let lossValuesCritic = []; let lossValuesActor = []; console.time("Training"); diff --git a/demo/webapp/public/js/DDPG/index.js b/demo/webapp/public/js/DDPG/index.js index 2810b85..6ceed76 100644 --- a/demo/webapp/public/js/DDPG/index.js +++ b/demo/webapp/public/js/DDPG/index.js @@ -22,7 +22,7 @@ env.loop(() => { displayState("realtime_viewer", state.lidar, 200, 200); let reward = env.getLastReward(); - const qValue = agent.getQvalue(state.linear, [state.steering]); + const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]); }); diff --git a/demo/webapp/public/js/DDPG/noise.js b/demo/webapp/public/js/DDPG/noise.js index 9c0ebb2..08c8978 100644 --- a/demo/webapp/public/js/DDPG/noise.js +++ b/demo/webapp/public/js/DDPG/noise.js @@ -16,8 +16,8 @@ class AdaptiveParamNoiseSpec { */ constructor(conf){ conf = conf || {}; - this.initialStddev = conf.initialStddev || 0.3; - this.desiredActionStddev = conf.desiredActionStddev || 0.3; + this.initialStddev = conf.initialStddev || 0.4; + this.desiredActionStddev = conf.desiredActionStddev || 0.4; this.adoptionCoefficient = conf.adoptionCoefficient || 1.01; this.currentStddev = this.initialStddev; } diff --git a/src/level.ts b/src/level.ts index 605640c..f7f12f2 100644 --- a/src/level.ts +++ b/src/level.ts @@ -110,8 +110,7 @@ export class Level extends World { } setReward(agent_col: any, on_road: any, action: any){ - let reward = 0; - //let reward = -0.8 + this.agent.core.v / this.agent.motion.maxSpeed; + let reward = 0 + Math.max(0., this.agent.core.v) / this.agent.motion.maxSpeed; if (agent_col.length > 0){ reward = -1; } From ff27eae6dd638ec5c59b0a87c4afb363eaf988f2 Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Mon, 25 Jun 2018 16:35:45 +0100 Subject: [PATCH 5/9] Model dumps --- demo/webapp/public/js/DDPG/ddpg.js | 19 ---------------- demo/webapp/public/js/DDPG/ddpg_agent.js | 16 +++++++++++++ demo/webapp/public/js/DDPG/index.js | 21 +++++------------- demo/webapp/public/js/level1.js | 2 +- .../models/ddpg/actor-model-ddpg-agent.json | 1 + .../ddpg/actor-model-ddpg-agent.weights.bin | Bin 0 -> 13192 bytes .../models/ddpg/critic-model-ddpg-agent.json | 1 + .../ddpg/critic-model-ddpg-agent.weights.bin | Bin 0 -> 13828 bytes 8 files changed, 25 insertions(+), 35 deletions(-) create mode 100644 demo/webapp/public/models/ddpg/actor-model-ddpg-agent.json create mode 100644 demo/webapp/public/models/ddpg/actor-model-ddpg-agent.weights.bin create mode 100644 demo/webapp/public/models/ddpg/critic-model-ddpg-agent.json create mode 100644 demo/webapp/public/models/ddpg/critic-model-ddpg-agent.weights.bin diff --git a/demo/webapp/public/js/DDPG/ddpg.js b/demo/webapp/public/js/DDPG/ddpg.js index 3655528..63fd1cc 100644 --- a/demo/webapp/public/js/DDPG/ddpg.js +++ b/demo/webapp/public/js/DDPG/ddpg.js @@ -211,26 +211,7 @@ class DDPG { return tf.mean(tfQPredictions0).mul(tf.scalar(-1.)) }, true, this.actorWeights); - /* - const sanityTfLoss = tf.tidy(() => { - const tfQPredictions0 = this.criticTargetWithActor(tfObs0); - const mn = tf.mean(tfQPredictions0); - const loss = mn.mul(tf.scalar(-1)); - return loss; - }); - - const sanityLoss = sanityTfLoss.buffer().values[0]; - - - if (sanityLoss == loss){ - console.warn("Sanity check failed. The optimisation have no effet here."); - } - */ - - //if (this.trainActorCt % 200 == 0 && this.trainActorCt != 0){ targetUpdate(this.actorTarget, this.actor, this.config); - //} - //this.trainActorCt += 1; const loss = actorLoss.buffer().values[0]; actorLoss.dispose(); diff --git a/demo/webapp/public/js/DDPG/ddpg_agent.js b/demo/webapp/public/js/DDPG/ddpg_agent.js index 553575b..2a6050c 100644 --- a/demo/webapp/public/js/DDPG/ddpg_agent.js +++ b/demo/webapp/public/js/DDPG/ddpg_agent.js @@ -51,6 +51,22 @@ class DDPGAgent { this.ddpg = new DDPG(this.actor, this.critic, this.memory, this.noise, this.config); } + save(env){ + /* + Save the network + */ + this.ddpg.critic.model.save('downloads://critic-model-ddpg-agent'); + this.ddpg.actor.model.save('downloads://actor-model-ddpg-agent'); + } + + async restore(){ + /* + Restore the weights of the network + */ + this.ddpg.critic.model = await tf.loadModel('http://localhost:3000/public/models/ddpg/critic-model-ddpg-agent.json'); + this.ddpg.actor.model = await tf.loadModel("http://localhost:3000/public/models/ddpg/actor-model-ddpg-agent.json"); + } + /** * Play one step */ diff --git a/demo/webapp/public/js/DDPG/index.js b/demo/webapp/public/js/DDPG/index.js index 6ceed76..2eacb40 100644 --- a/demo/webapp/public/js/DDPG/index.js +++ b/demo/webapp/public/js/DDPG/index.js @@ -51,10 +51,6 @@ env.load().then(() => { RECORD = false; }); - env.addEvent("trainOnRecord", () => { - agent.ddpg.trainRecord(); - }); - env.addEvent("TrainRealTime", () => { env.steping(false); agent.train(true); @@ -66,17 +62,12 @@ env.load().then(() => { env.addEvent("reset_env"); - env.addEvent("load", (content) => { - content = JSON.parse(content); - - for (let c=0; c < content.obs0.length; c++){ - if (content.obs0[c] != 0){ - agent.ddpg.memory.append(content.obs0[c], content.actions[c], content.rewards[c], content.obs0[c], content.terminals[c]); - } - } - - console.log("dataReady"); + env.addEvent("save", () => { + agent.save(); + }); - }, {local: true}); + env.addEvent("load", () => { + agent.restore() + }); }); diff --git a/demo/webapp/public/js/level1.js b/demo/webapp/public/js/level1.js index 513f8de..c40dc16 100644 --- a/demo/webapp/public/js/level1.js +++ b/demo/webapp/public/js/level1.js @@ -30,6 +30,6 @@ env.load().then(() => { env.addEvent("save", () => agent.save()); env.addEvent("load", () => { document.getElementById("metacar_canvas_button_train").style.display = "none"; - agent.restore() + agent.restore(); }); }); diff --git a/demo/webapp/public/models/ddpg/actor-model-ddpg-agent.json b/demo/webapp/public/models/ddpg/actor-model-ddpg-agent.json new file mode 100644 index 0000000..3cc61fd --- /dev/null +++ b/demo/webapp/public/models/ddpg/actor-model-ddpg-agent.json @@ -0,0 +1 @@ +{"modelTopology":{"class_name":"Model","config":{"name":"model1","layers":[{"name":"input1","class_name":"InputLayer","config":{"batch_input_shape":[null,17],"dtype":"float32","sparse":false,"name":"input1"},"inbound_nodes":[]},{"name":"dense_Dense1","class_name":"Dense","config":{"units":64,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true},"inbound_nodes":[[["input1",0,0,{}]]]},{"name":"dense_Dense2","class_name":"Dense","config":{"units":32,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true},"inbound_nodes":[[["dense_Dense1",0,0,{}]]]},{"name":"dense_Dense3","class_name":"Dense","config":{"units":2,"activation":"tanh","use_bias":true,"kernel_initializer":{"class_name":"RandomUniform","config":{"minval":0.003,"maxval":0.003,"seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense3","trainable":true},"inbound_nodes":[[["dense_Dense2",0,0,{}]]]}],"input_layers":[["input1",0,0]],"output_layers":[["dense_Dense3",0,0]]},"keras_version":"tfjs-layers 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ddpg}/noise.js | 0 .../index.js} | 2 +- .../{ => policy_monte_carlo}/policy_agent.js | 7 +- .../public/js/{level0.js => q_table/index.js} | 4 +- .../public/js/{ => q_table}/q_table_agent.js | 6 +- demo/webapp/public/js/traffic/index.js | 67 +++++ .../actor-model-ddpg-road.json} | 2 +- .../actor-model-ddpg-road.weights.bin | Bin 0 -> 15496 bytes .../critic-model-ddpg-road.json} | 2 +- .../critic-model-ddpg-road.weights.bin | Bin 0 -> 16132 bytes .../actor-model-ddpg-traffic.json | 1 + .../actor-model-ddpg-traffic.weights.bin | Bin 0 -> 21640 bytes .../critic-model-ddpg-traffic.json | 1 + .../critic-model-ddpg-traffic.weights.bin | Bin 0 -> 22276 bytes .../ddpg/actor-model-ddpg-agent.weights.bin | Bin 13192 -> 0 bytes .../ddpg/critic-model-ddpg-agent.weights.bin | Bin 13828 -> 0 bytes 23 files changed, 457 insertions(+), 308 deletions(-) create mode 100644 demo/webapp/public/img/ddpg.png delete mode 100644 demo/webapp/public/js/DDPG/memory.js rename demo/webapp/public/js/{DDPG => ddpg}/ddpg.js (74%) rename demo/webapp/public/js/{DDPG => ddpg}/ddpg_agent.js (66%) rename demo/webapp/public/js/{DDPG => ddpg}/index.js (56%) create mode 100644 demo/webapp/public/js/ddpg/memory.js rename demo/webapp/public/js/{DDPG => ddpg}/models.js (80%) rename demo/webapp/public/js/{DDPG => ddpg}/noise.js (100%) rename demo/webapp/public/js/{level1.js => policy_monte_carlo/index.js} (96%) rename demo/webapp/public/js/{ => policy_monte_carlo}/policy_agent.js (96%) rename demo/webapp/public/js/{level0.js => q_table/index.js} (92%) rename demo/webapp/public/js/{ => q_table}/q_table_agent.js (96%) create mode 100644 demo/webapp/public/js/traffic/index.js rename demo/webapp/public/models/{ddpg/actor-model-ddpg-agent.json => ddpg-road/actor-model-ddpg-road.json} (79%) create mode 100644 demo/webapp/public/models/ddpg-road/actor-model-ddpg-road.weights.bin rename demo/webapp/public/models/{ddpg/critic-model-ddpg-agent.json => ddpg-road/critic-model-ddpg-road.json} (80%) create mode 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console.error("Memory.getItem: idx not in range."); - } - return this.data[(this.start + idx) % this.maxlen] - } - - /** - * Sample a batch - * @param batchSize (number) - * @return batch [] - */ - getBatch(batchSize){ - const arrLength = this.length; - const batch = { - 'obs0': [], - 'obs1': [], - 'rewards': [], - 'actions': [], - 'terminals': [], - }; - if (batchSize > this.length){ - return batch; - } - for (let b=0; b < batchSize; b++){ - let id = Math.floor(Math.random() * arrLength); - batch.obs0.push(this.obs0List[id]); - batch.obs1.push(this.obs1List[id]); - batch.rewards.push(this.rewardsList[id]); - batch.actions.push(this.actionsList[id]); - batch.terminals.push(this.terminals1List[id]); - } - return batch - } - - /** - * @param obs0 [] - * @param action (number) - * @param reward (number) - * @param obs1 [] - * @param terminal1 (boolean) - */ - append(obs0, action, reward, obs1, terminal1){ - if (this.length < this.maxlen){ - this.length += 1; - } - else if (this.length == this.maxlen) { - //this.obs0List[(this.start + this.length - 1) % this.maxlen].dispose(); - //this.obs1List[(this.start + this.length - 1) % this.maxlen].dispose(); - //this.actionsList[(this.start + this.length - 1) % this.maxlen].dispose(); - this.start = (this.start + 1) % this.maxlen; - } - else { - console.error("Memory.append: This should never be printed"); - } - this.obs0List[(this.start + this.length - 1) % this.maxlen] = obs0; - this.obs1List[(this.start + this.length - 1) % this.maxlen] = obs1; - this.rewardsList[(this.start + this.length - 1) % this.maxlen] = reward; - this.actionsList[(this.start + this.length - 1) % this.maxlen] = action; - this.terminals1List[(this.start + this.length - 1) % this.maxlen] = terminal1; - } -} \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/ddpg.js b/demo/webapp/public/js/ddpg/ddpg.js similarity index 74% rename from demo/webapp/public/js/DDPG/ddpg.js rename to demo/webapp/public/js/ddpg/ddpg.js index 63fd1cc..c55c0a2 100644 --- a/demo/webapp/public/js/DDPG/ddpg.js +++ b/demo/webapp/public/js/ddpg/ddpg.js @@ -29,13 +29,6 @@ class DDPG { let obsInput = tf.input({batchShape: [null, this.config.stateSize]}); let actionInput = tf.input({batchShape: [null, this.config.nbActions]}); - if (config.normalizeObservations){ - tf.layers.batchNormalization({ - scale: true, - center: true - }).apply(obsInput); - } - // Randomly Initialize actor network μ(s) this.actor.buildModel(obsInput); // Randomly Initialize critic network Q(s, a) @@ -56,7 +49,6 @@ class DDPG { return this.critic.predict(tfState, tfAct); }); }; - this.criticTargetWithActorTarget = (tfState) => { return tf.tidy(() => { const tfAct = this.actorTarget.predict(tfState); @@ -64,7 +56,6 @@ class DDPG { }); }; - this.actorOptimiser = tf.train.adam(this.config.actorLr); this.criticOptimiser = tf.train.adam(this.config.criticLr); @@ -72,23 +63,19 @@ class DDPG { for (let w = 0; w < this.critic.model.trainableWeights.length; w++){ this.criticWeights.push(this.critic.model.trainableWeights[w].val); } - this.actorWeights = []; for (let w = 0; w < this.actor.model.trainableWeights.length; w++){ this.actorWeights.push(this.actor.model.trainableWeights[w].val); } assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev, this.config.seed); - - this.trainActorCt = 0; - this.trainCriticCt = 0; } /** * Distance Measure for DDPG * See parameter space noise Exploration paper - * obs (Tensor2d) Observations + * @param observations (Tensor2d) Observations */ distanceMeasure(observations) { return tf.tidy(() => { @@ -105,6 +92,11 @@ class DDPG { */ adaptParamNoise(){ const batch = this.memory.getBatch(this.config.batchSize); + if (batch.obs0.length == 0){ + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev, this.config.seed); + return [0]; + } + let distanceV = null; if (batch.obs0.length > 0){ @@ -123,17 +115,6 @@ class DDPG { return distanceV; } - /** - * Eval the actions and the Q function - * @param states (tf.tensor2d) - * @return actions and qValues - */ - eval(observation){ - const tfActions = this.perturbedActor.model.predict(observation); - const tfQValues = this.critic.model.predict([observation, tfActions]); - return {tfActions, tfQValues}; - } - /** * Get the estimation of the Q value given the state * and the action @@ -174,13 +155,13 @@ class DDPG { */ targetUpdate(){ // Define in js/DDPG/models.js - //assignModel(this.critic, this.criticTarget); - //assignModel(this.actor, this.actorTarget); targetUpdate(this.criticTarget, this.critic, this.config); targetUpdate(this.actorTarget, this.actor, this.config); } - trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals){ + trainCritic(batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals){ + + let costs; const criticLoss = this.criticOptimiser.minimize(() => { const tfQPredictions0 = this.critic.model.predict([tfObs0, tfActions]); @@ -188,18 +169,18 @@ class DDPG { const tfQTargets = tfRewards.add(tf.scalar(1).sub(tfTerminals).mul(this.tfGamma).mul(tfQPredictions1)); - return tf.sub(tfQTargets, tfQPredictions0).square().mean(); + const erros = tf.sub(tfQTargets, tfQPredictions0).square(); + costs = erros.buffer().values; + + return erros.mean(); }, true, this.criticWeights); + this.memory.appendBackWithCost(batch, costs); + const loss = criticLoss.buffer().values[0]; criticLoss.dispose(); - //if (this.trainCriticCt % 200 == 0 && this.trainCriticCt != 0){ targetUpdate(this.criticTarget, this.critic, this.config); - //console.log("Update"); - // Saniity Check - //} - this.trainCriticCt += 1; return loss; } @@ -215,14 +196,13 @@ class DDPG { const loss = actorLoss.buffer().values[0]; actorLoss.dispose(); - //sanityTfLoss.dispose(); return loss; } getTfBatch(){ // Get batch - const batch = this.memory.getBatch(this.config.batchSize); + const batch = this.memory.popBatch(this.config.batchSize); // Convert to tensors const tfActions = tf.tensor2d(batch.actions); const tfObs0 = tf.tensor2d(batch.obs0); @@ -237,12 +217,12 @@ class DDPG { _tfTerminals.dispose(); return { - tfActions, tfObs0, tfObs1, tfRewards, tfTerminals + batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals } } optimizeCritic(){ - const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + const {batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); const loss = this.trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); @@ -255,10 +235,10 @@ class DDPG { return loss; } - optimizeActor(it=1){ - const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + optimizeActor(){ + const {batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); - const loss = this.trainActor(tfObs0, it); + const loss = this.trainActor(tfObs0); tfActions.dispose(); tfObs0.dispose(); @@ -270,9 +250,9 @@ class DDPG { } optimizeCriticActor(){ - const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + const {batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); - const lossC = this.trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); + const lossC = this.trainCritic(batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); const lossA = this.trainActor(tfObs0); tfActions.dispose(); @@ -283,32 +263,4 @@ class DDPG { return {lossC, lossA}; } - - trainRecord(){ - - let lossValues = []; - - for (let i=0; i < 32; i++){ - - const {tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); - - const actorLoss = this.actorOptimiser.minimize(() => { - const tfAPredictions0 = this.actor.model.predict(tfObs0); - const loss = tfActions.sub(tfAPredictions0).square().mean(); - return loss; - }, true, this.actorWeights); - - const loss = actorLoss.buffer().values[0]; - lossValues.push(loss); - - actorLoss.dispose(); - tfActions.dispose(); - tfObs0.dispose(); - tfObs1.dispose(); - tfRewards.dispose(); - tfTerminals.dispose(); - } - console.log("Mean loss", mean(lossValues)); - } - } \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/ddpg_agent.js b/demo/webapp/public/js/ddpg/ddpg_agent.js similarity index 66% rename from demo/webapp/public/js/DDPG/ddpg_agent.js rename to demo/webapp/public/js/ddpg/ddpg_agent.js index 2a6050c..0906222 100644 --- a/demo/webapp/public/js/DDPG/ddpg_agent.js +++ b/demo/webapp/public/js/ddpg/ddpg_agent.js @@ -1,36 +1,45 @@ - // This class is called from js/DDPG/index.js class DDPGAgent { /** * @param env (metacar.env) Set in js/DDPG/index.js */ - constructor(env){ + constructor(env, config){ + this.stopTraining = false; this.env = env; + + config = config || {}; // Default Config this.config = { - "stateSize": 17, - "nbActions": 2, - "layerNorm": false, - "normalizeObservations": true, - "seed": 0, - "criticL2Reg": 0.01, - "batchSize": 64, - "actorLr": 0.0001, - "criticLr": 0.001, - "memorySize": 20000, - "gamma": 0.99, - "noiseDecay": 0.99, - "rewardScale": 1, - "nbEpochs": 500, - "nbEpochsCycle": 20, - "nbTrainSteps": 50, - "tau": 0.01, - "paramNoiseAdaptionInterval": 50, + "stateSize": config.stateSize || 17, + "nbActions": config.nbActions || 2, + "seed": config.seed || 0, + "batchSize": config.batchSize || 128, + "actorLr": config.actorLr || 0.0001, + "criticLr": config .criticLr || 0.001, + "memorySize": config.memorySize || 30000, + "gamma": config.gamme || 0.99, + "noiseDecay": config.noiseDecay || 0.99, + "rewardScale": config.rewardScale || 1, + "nbEpochs": config.nbEpochs || 200, + "nbEpochsCycle": config.nbEpochsCycle || 10, + "nbTrainSteps": config.nbTrainSteps || 110, + "tau": config.tau || 0.01, + "initialStddev": config.initialStddev || 0.1, + "desiredActionStddev": config.desiredActionStddev || 0.1, + "adoptionCoefficient": config.adoptionCoefficient || 1.01, + "actorFirstLayerSize": config.actorFirstLayerSize || 64, + "actorSecondLayerSize": config.actorSecondLayerSize || 32, + "criticFirstLayerSSize": config.criticFirstLayerSSize || 64, + "criticFirstLayerASize": config.criticFirstLayerASize || 64, + "criticSecondLayerSize": config.criticSecondLayerSize || 32, + "maxStep": config.maxStep || 800, + "stopOnRewardError": config.stopOnRewardError != undefined ? config.stopOnRewardError:true, + "resetEpisode": config.resetEpisode != undefined ? config.resetEpisode:false }; // From js/DDPG/noise.js - this.noise = new AdaptiveParamNoiseSpec(); + this.noise = new AdaptiveParamNoiseSpec(this.config); // Configure components. @@ -51,20 +60,20 @@ class DDPGAgent { this.ddpg = new DDPG(this.actor, this.critic, this.memory, this.noise, this.config); } - save(env){ + save(name){ /* Save the network */ - this.ddpg.critic.model.save('downloads://critic-model-ddpg-agent'); - this.ddpg.actor.model.save('downloads://actor-model-ddpg-agent'); + this.ddpg.critic.model.save('downloads://critic-' + name); + this.ddpg.actor.model.save('downloads://actor-'+ name); } - async restore(){ + async restore(folder, name){ /* Restore the weights of the network */ - this.ddpg.critic.model = await tf.loadModel('http://localhost:3000/public/models/ddpg/critic-model-ddpg-agent.json'); - this.ddpg.actor.model = await tf.loadModel("http://localhost:3000/public/models/ddpg/actor-model-ddpg-agent.json"); + this.ddpg.critic.model = await tf.loadModel('http://localhost:3000/public/models/'+folder+'/critic-'+name+'.json'); + this.ddpg.actor.model = await tf.loadModel("http://localhost:3000/public/models/"+folder+"/actor-"+name+".json"); } /** @@ -103,11 +112,6 @@ class DDPGAgent { stepTrain(tfPreviousStep, mPreviousStep){ // Get actions const tfActions = this.ddpg.perturbedPrediction(tfPreviousStep); - //const TruetfActions = this.ddpg.perturbedPrediction(tfPreviousStep); - //const rdNormal = tf.randomNormal(TruetfActions.shape, 0, this.noisyActions, "float32", this.config.seed); - //const noisyActions = TruetfActions.add(rdNormal); - //const tfActions = tf.clipByValue(noisyActions, -1, 1); - // Step in the environment with theses actions let mAcions = tfActions.buffer().values; let mReward = this.env.step([mAcions[0], mAcions[1]]); @@ -116,43 +120,31 @@ class DDPGAgent { let mState = this.env.getState().linear; let tfState = tf.tensor2d([mState]); let mDone = 0; - - if (mReward == -1){ + if (mReward == -1 && this.config.stopOnRewardError){ mDone = 1; } - // Add the new tuple to the buffer this.ddpg.memory.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone); - - // Dispose tensor + // Dispose tensors tfPreviousStep.dispose(); - //TruetfActions.dispose(); - //noisyActions.dispose(); tfActions.dispose(); - //rdNormal.dispose(); - //rd.dispose(); - //trueTfActions.dispose(); - return {mDone, mState, tfState} - } - initTrainParam(){ - this.stopTraining = false; - this.noisyActions = 2.0; + return {mDone, mState, tfState}; } /** * Train DDPG Agent */ async train(realTime){ - this.initTrainParam(); + this.stopTraining = false; // One epoch for (let e=0; e < this.config.nbEpochs; e++){ // Perform cycles. this.rewardsList = []; this.stepList = []; this.distanceList = []; + document.getElementById("trainingProgress").innerHTML = "Progression: "+e+"/"+this.config.nbEpochs+"
"; for (let c=0; c < this.config.nbEpochsCycle; c++){ - if (c%10==0){ logTfMemory(); } @@ -164,11 +156,11 @@ class DDPGAgent { let step = 0; console.time("LoopTime"); - for (step=0; step < 800; step++){ + for (step=0; step < this.config.maxStep; step++){ let rel = this.stepTrain(tfPreviousStep, mPreviousStep); mPreviousStep = rel.mState; tfPreviousStep = rel.tfState; - if (rel.mDone){ + if (rel.mDone && this.config.stopOnRewardError){ break; } if (this.stopTraining){ @@ -183,6 +175,9 @@ class DDPGAgent { let distance = this.ddpg.adaptParamNoise(); this.distanceList.push(distance[0]); + if (this.config.resetEpisode){ + this.env.reset(); + } this.env.randomRoadPosition(); tfPreviousStep.dispose(); console.log("e="+ e +", c="+c); @@ -190,28 +185,25 @@ class DDPGAgent { //this.ddpg.targetUpdate(); await tf.nextFrame(); } - if (this.ddpg.memory.length == this.config.memorySize){ - this.noisyActions = Math.max(0.1, this.noisyActions * this.config.noiseDecay); + if (e > 5){ this.ddpg.noise.desiredActionStddev = Math.max(0.1, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev); let lossValuesCritic = []; let lossValuesActor = []; - console.time("Training"); - for (let t=0; t < 100; t++){ - let lossC = this.ddpg.optimizeCritic(); + console.time("Training"); + for (let t=0; t < this.config.nbTrainSteps; t++){ + let {lossC, lossA} = this.ddpg.optimizeCriticActor(); lossValuesCritic.push(lossC); - } - for (let t=0; t < 100; t++){ - let lossA = this.ddpg.optimizeActor(); lossValuesActor.push(lossA); } console.timeEnd("Training"); console.log("desiredActionStddev:", this.ddpg.noise.desiredActionStddev); setMetric("CriticLoss", mean(lossValuesCritic)); setMetric("ActorLoss", mean(lossValuesActor)); - } + } + setMetric("Reward", mean(this.rewardsList)); setMetric("EpisodeDuration", mean(this.stepList)); - setMetric("Distance", mean(this.distanceList)); + setMetric("NoiseDistance", mean(this.distanceList)); await tf.nextFrame(); } diff --git a/demo/webapp/public/js/DDPG/index.js b/demo/webapp/public/js/ddpg/index.js similarity index 56% rename from demo/webapp/public/js/DDPG/index.js rename to demo/webapp/public/js/ddpg/index.js index 2eacb40..07e5927 100644 --- a/demo/webapp/public/js/DDPG/index.js +++ b/demo/webapp/public/js/ddpg/index.js @@ -2,58 +2,47 @@ let levelUrl = metacar.level.level2; var env = new metacar.env("canvas", levelUrl); -env.setAgentMotion(metacar.motion.ControlMotion, {}); -env.setAgentLidar({pts: 4, width: 2, height: 9, pos: 1}) - -RECORD = false; +env.setAgentMotion(metacar.motion.ControlMotion, {}); +env.setAgentLidar({pts: 5, width: 3, height: 7, pos: -0.5}) // js/DDPG/ddpg.js -var agent = new DDPGAgent(env); +var agent = new DDPGAgent(env, { + stateSize: 26, + resetEpisode: true +}); -initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "Distance"]); +initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); env.loop(() => { let state = env.getState(); - - if (RECORD){ - agent.ddpg.memory.append(state.linear, [state.a, state.steering], 1, [1, 1, 1], 1); - } - displayState("realtime_viewer", state.lidar, 200, 200); let reward = env.getLastReward(); const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]); }); - env.load().then(() => { - - // Train agent - env.addEvent("train", () => { - agent.train(false); - }); - - env.addEvent("play", () => { - agent.play(); + env.addEvent("train [Background]", () => { + let train = confirm("The training process takes some time and might slow this tab. Do you want to continue? \n You can also load a pre-trained model."); + if (train){ + env.render(false); + agent.train(false); + } }); - env.addEvent("record", () => { - RECORD = true; + env.addEvent("Train [Show the training]", () => { + env.steping(false); + agent.train(true); }); - env.addEvent("randomPos", () => { + env.addEvent("shuffle", () => { env.randomRoadPosition(); - }); - - env.addEvent("stopRecord", () => { - RECORD = false; - }); - - env.addEvent("TrainRealTime", () => { - env.steping(false); - agent.train(true); + }) + + env.addEvent("play", () => { + agent.play(); }); env.addEvent("stop", () => { @@ -63,11 +52,11 @@ env.load().then(() => { env.addEvent("reset_env"); env.addEvent("save", () => { - agent.save(); + agent.save("model-ddpg-road"); }); env.addEvent("load", () => { - agent.restore() + agent.restore("ddpg-road", "model-ddpg-road") }); }); diff --git a/demo/webapp/public/js/ddpg/memory.js b/demo/webapp/public/js/ddpg/memory.js new file mode 100644 index 0000000..b4921fb --- /dev/null +++ b/demo/webapp/public/js/ddpg/memory.js @@ -0,0 +1,240 @@ + +class Memory { + + /** + * @param maxlen (number) Buffer limit + */ + constructor(maxlen){ + this.maxlen = maxlen; + this.buffer = []; + this.priorBuffer = []; + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + getBatch(batchSize){ + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + + if (batchSize > this.priorBuffer.length){ + console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); + return batch; + } + + for (let b=0; b < batchSize/2; b++){ + let id = Math.floor(Math.random() * this.priorBuffer.length); + batch.obs0.push(this.priorBuffer[id].obs0); + batch.obs1.push(this.priorBuffer[id].obs1); + batch.rewards.push(this.priorBuffer[id].reward); + batch.actions.push(this.priorBuffer[id].action); + batch.terminals.push(this.priorBuffer[id].terminal); + } + return batch + } + + _bufferBatch(batchSize){ + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + + for (let b=0; b < batchSize/2; b++){ + let nElem = this.buffer.pop(); + batch.obs0.push(nElem.obs0); + batch.obs1.push(nElem.obs1); + batch.rewards.push(nElem.reward); + batch.actions.push(nElem.action); + batch.terminals.push(nElem.terminal); + } + + for (let b=0; b < batchSize/2; b++){ + let id = Math.floor(Math.random() * this.buffer.length); + batch.obs0.push(this.buffer[id].obs0); + batch.obs1.push(this.buffer[id].obs1); + batch.rewards.push(this.buffer[id].reward); + batch.actions.push(this.buffer[id].action); + batch.terminals.push(this.buffer[id].terminal); + this.buffer.splice(id, 1); + } + + return batch + } + + _addRandomBufferBatch(batchSize, batch){ + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * this.buffer.length); + batch.obs0.push(this.buffer[id].obs0); + batch.obs1.push(this.buffer[id].obs1); + batch.rewards.push(this.buffer[id].reward); + batch.actions.push(this.buffer[id].action); + batch.terminals.push(this.buffer[id].terminal); + this.buffer.splice(id, 1); + } + return batch + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + popBatch(batchSize){ + let originalBatchSize = batchSize; + let priorBufferBatchSize; + let bufferBatchSize; + if (batchSize % 2 != 0){ + console.warn("Batch size should be a even.") + } + if (this.priorBuffer.length < batchSize/2){ + //console.log("get full batch from buffer"); + const batch = this._bufferBatch(batchSize); + console.assert(batch.obs0.length == batchSize); + return batch; + } + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + if (batchSize > this.length){ + console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); + return batch; + } + + if (this.buffer.length > 0){ + //console.log("Get half of prior and other from buffer."); + batchSize = batchSize / 2; + } + else{ + //console.log("Get all from priorBuffer"); + } + + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * this.priorBuffer.length); + batch.obs0.push(this.priorBuffer[id].obs0); + batch.obs1.push(this.priorBuffer[id].obs1); + batch.rewards.push(this.priorBuffer[id].reward); + batch.actions.push(this.priorBuffer[id].action); + batch.terminals.push(this.priorBuffer[id].terminal); + this.priorBuffer.splice(id, 1); + } + + if (this.buffer.length > 0){ + this._addRandomBufferBatch(batchSize, batch); + } + console.assert(batch.obs0.length == originalBatchSize); + return batch + } + + _insert(element, array) { + if (array.length == 0 || element.cost < array[0].cost || array[0].cost == null){ + array.unshift(element); + return array; + } + array.splice(this._locationOf(element, array) + 1, 0, element); + return array; + } + + _locationOf(element, array, start, end) { + start = start || 0; + end = end || array.length; + + var pivot = parseInt(start + (end - start) / 2, 10); + + if (end-start <= 1 || array[pivot] === element) return pivot; + + if (array[pivot].cost != null && array[pivot].cost < element.cost) { + return this._locationOf(element, array, pivot, end); + } else { + return this._locationOf(element, array, start, pivot); + } + } + + /** + * @param batch (Object) from getBatch() + * @param cost (number) Cost associated with each row of the batch + */ + appendBackWithCost(batch, costs){ + for (let b=0; b < batch.obs0.length; b++){ + if (this.buffer.length == this.maxlen){ + this.buffer.shift(); + } + this._insert({ + obs0: batch.obs0[b], + action: batch.actions[b], + reward: batch.rewards[b], + obs1: batch.obs1[b], + terminal: batch.terminals[b], + cost: costs[b] + }, this.buffer); + } + console.assert(this.buffer.length <= this.maxlen); + } + + /** + * @param obs0 [] + * @param action (number) + * @param reward (number) + * @param obs1 [] + * @param terminal1 (boolean) + */ + append(obs0, action, reward, obs1, terminal){ + if (this.priorBuffer.length == this.maxlen){ + this.priorBuffer.shift(); + } + this.priorBuffer.push({ + obs0: obs0, + action: action, + reward: reward, + obs1: obs1, + terminal: terminal, + cost: null + }); + console.assert(this.priorBuffer.length <= this.maxlen); + } +} +/* +var mem = new Memory(20000); +Math.seedrandom(0); +console.assert(mem.length == 0); + +var array = []; +for (let i=1; i < 40000; i++){ + mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); +} + +console.assert(mem.length == 20000); +console.assert(mem.list[0].obs0 == "obs0-20000"); +console.assert(mem.list[19999].obs0 == "obs0-39999"); + +let batch = mem.getBatch(32); + +console.assert(batch.obs0.length == 32); +console.assert(mem.length == 20000 - 32); + +let costs = []; +for (i=31; i >= 0; i--){ + costs.push(i); +} +mem.appendBackWithCost(batch, costs); + +console.log(mem.list); + +/* +for (let i=1; i < 64; i++){ + mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); +} +*/ \ No newline at end of file diff --git a/demo/webapp/public/js/DDPG/models.js b/demo/webapp/public/js/ddpg/models.js similarity index 80% rename from demo/webapp/public/js/DDPG/models.js rename to demo/webapp/public/js/ddpg/models.js index 65c0edb..7823acc 100644 --- a/demo/webapp/public/js/DDPG/models.js +++ b/demo/webapp/public/js/ddpg/models.js @@ -18,45 +18,29 @@ function copyModel(model, instance){ } /** - * Usefull method to copy a model - * @param model Actor - * @param perturbedActor Actor + * Copy the value of of the model into the perturbedModel and + * add a random pertubation + * @param model Actor|Critic instance + * @param perturbedActor Actor|Critic instance * @param stddev (number) * @return Copy of the model */ -function assignAndStd(actor, perturbedActor, stddev, seed){ +function assignAndStd(model, perturbedModel, stddev, seed){ return tf.tidy(() => { - const weights = actor.model.trainableWeights; + const weights = model.model.trainableWeights; for (let m=0; m < weights.length; m++){ - let shape = perturbedActor.model.trainableWeights[m].val.shape; + let shape = perturbedModel.model.trainableWeights[m].val.shape; let randomTensor = tf.randomNormal(shape, 0, stddev, "float32", seed); let nValue = weights[m].val.add(randomTensor); - perturbedActor.model.trainableWeights[m].val.assign(nValue); + perturbedModel.model.trainableWeights[m].val.assign(nValue); } }); } /** - * Usefull method to copy a model - * @param model Actor - * @param perturbedActor Actor - * @return Copy of the model - */ -function assignModel(model, targetModel){ - return tf.tidy(() => { - const weights = model.model.trainableWeights; - for (let m=0; m < weights.length; m++){ - let nValue = weights[m].val; - targetModel.model.trainableWeights[m].val.assign(nValue); - } - }); -} - - -/** - * Usefull method to copy a model - * @param target Actor|Critic - * @param perturbedActor Actor|Critic + * Update the target models + * @param target Actor|Critic instance + * @param perturbedActor Actor|Critic instance * @param config (Object) * @return Copy of the model */ @@ -90,6 +74,10 @@ class Actor{ this.stateSize = config.stateSize; this.nbActions = config.nbActions; this.layerNorm = config.layerNorm; + + this.firstLayerSize = config.actorFirstLayerSize; + this.secondLayerSize = config.actorSecondLayerSize; + this.seed = config.seed; this.config = config; this.obs = null; @@ -102,24 +90,22 @@ class Actor{ buildModel(obs){ this.obs = obs; - // First layer with BatchNormalization + // First layer this.firstLayer = tf.layers.dense({ - units: 64, + units: this.firstLayerSize, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), - activation: 'relu', // relu is add later + activation: 'relu', useBias: true, biasInitializer: "zeros" }); - - // Second layer with BatchNormalization + // Second layer this.secondLayer = tf.layers.dense({ - units: 32, + units: this.secondLayerSize, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), - activation: 'relu', // relu is add later + activation: 'relu', useBias: true, biasInitializer: "zeros" }); - // Ouput layer this.outputLayer = tf.layers.dense({ units: this.nbActions, @@ -129,12 +115,13 @@ class Actor{ useBias: true, biasInitializer: "zeros" }); - + // Actor prediction this.predict = (tfState) => { return tf.tidy(() => { if (tfState){ obs = tfState; } + let l1 = this.firstLayer.apply(obs); let l2 = this.secondLayer.apply(l1); @@ -155,6 +142,11 @@ class Critic { this.stateSize = config.stateSize; this.nbActions = config.nbActions; this.layerNorm = config.layerNorm; + + this.firstLayerSSize = config.criticFirstLayerSSize + this.firstLayerASize = config.criticFirstLayerASize; + this.secondLayerSize = config.criticSecondLayerSize; + this.seed = config.seed; this.config = config; this.obs = null; @@ -170,30 +162,28 @@ class Critic { this.obs = obs; this.action = action; + // Used to merged the two first Layer later. this.add = tf.layers.add(); - // First layer with BatchNormalization + // First layer this.firstLayerS = tf.layers.dense({ - units: 64, + units: this.firstLayerSSize, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'linear', // relu is add later useBias: true, biasInitializer: "zeros" }); - - // First layer with BatchNormalization + // First layer this.firstLayerA = tf.layers.dense({ - units: 64, + units: this.firstLayerASize, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'linear', // relu is add later useBias: true, biasInitializer: "zeros" }); - - // Second layer with BatchNormalization + // Second layer this.secondLayer = tf.layers.dense({ - //inputShape: [this.config.batchSize, 64 + this.nbActions], // Previous layer + action - units: 32, + units: this.secondLayerSize, kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), activation: 'relu', useBias: true, @@ -210,7 +200,7 @@ class Critic { biasInitializer: "zeros" }); - // Actor prediction + // Critic prediction this.predict = (tfState, tfActions) => { return tf.tidy(() => { if (tfState && tfActions){ @@ -220,7 +210,7 @@ class Critic { let l1A = this.firstLayerA.apply(action); let l1S = this.firstLayerS.apply(obs) - + // Merged layers let concat = this.add.apply([l1A, l1S]) let l2 = this.secondLayer.apply(concat); @@ -228,6 +218,7 @@ class Critic { return this.outputLayer.apply(l2); }); } + const output = this.predict(); this.model = tf.model({inputs: [obs, action], outputs: output}); } diff --git a/demo/webapp/public/js/DDPG/noise.js b/demo/webapp/public/js/ddpg/noise.js similarity index 100% rename from demo/webapp/public/js/DDPG/noise.js rename to demo/webapp/public/js/ddpg/noise.js diff --git a/demo/webapp/public/js/level1.js b/demo/webapp/public/js/policy_monte_carlo/index.js similarity index 96% rename from demo/webapp/public/js/level1.js rename to demo/webapp/public/js/policy_monte_carlo/index.js index c40dc16..b8b5632 100644 --- a/demo/webapp/public/js/level1.js +++ b/demo/webapp/public/js/policy_monte_carlo/index.js @@ -7,7 +7,7 @@ var env = new metacar.env("canvas", levelUrl); var agent = new PolicyAgent(env); env.loop(() => { - let state = env.getState(); + let state = env.getState().lidar; displayState("realtime_viewer", state, 200, 200); let scores = agent.getStateValues(state); let reward = env.getLastReward(); diff --git a/demo/webapp/public/js/policy_agent.js b/demo/webapp/public/js/policy_monte_carlo/policy_agent.js similarity index 96% rename from demo/webapp/public/js/policy_agent.js rename to demo/webapp/public/js/policy_monte_carlo/policy_agent.js index 08cc158..b8c8bb6 100644 --- a/demo/webapp/public/js/policy_agent.js +++ b/demo/webapp/public/js/policy_monte_carlo/policy_agent.js @@ -179,14 +179,12 @@ class PolicyAgent { */ this.valueModel = await tf.loadModel('https://metacar-project.com/public/models/policy/value-model-policy-agent.json'); this.policyModel = await tf.loadModel("https://metacar-project.com/public/models/policy/policy-model-policy-agent.json"); - //this.valueModel = await tf.loadModel('http://localhost:3000/public/models/policy/value-model-policy-agent.json'); - //this.policyModel = await tf.loadModel("http://localhost:3000/public/models/policy/policy-model-policy-agent.json"); } play(){ tf.tidy(() => { // Get the current state - const st = tf.tensor2d(this.env.getState(), [this.lidarPts, this.lidarPts]).reshape([1, this.ttLidarPts]); + const st = tf.tensor2d(this.env.getState().lidar, [this.lidarPts, this.lidarPts]).reshape([1, this.ttLidarPts]); // Predict the policy const softmax = this.policyModel.predict(st); // Get the action @@ -231,7 +229,8 @@ class PolicyAgent { console.time("Exploring"); for (var step = 0; step < this.nb_step; step++) { // Get the current state - const array_st = this.env.getState(true); + let array_st = this.env.getState().linear; + array_st = array_st.slice(0, array_st.length - 1); // Convert the state into a tensor //const st = tf.tensor(array_st, [this.lidarPts, this.lidarPts]).reshape([1, this.ttLidarPts]); const st = tf.tensor2d([array_st]); diff --git a/demo/webapp/public/js/level0.js b/demo/webapp/public/js/q_table/index.js similarity index 92% rename from demo/webapp/public/js/level0.js rename to demo/webapp/public/js/q_table/index.js index 4e44e77..4e6f42b 100644 --- a/demo/webapp/public/js/level0.js +++ b/demo/webapp/public/js/q_table/index.js @@ -12,8 +12,8 @@ var agent = new QTableAgent(env, 2); env.loop(() => { let state = env.getState(); - displayState("realtime_viewer", state, 200, 200); - let scores = agent.getStateValues(state); + displayState("realtime_viewer", state.lidar, 200, 200); + let scores = agent.getStateValues(state.lidar); let reward = env.getLastReward(); displayScores("realtime_viewer", scores, reward, ["Top", "Left", "Right"]); }); diff --git a/demo/webapp/public/js/q_table_agent.js b/demo/webapp/public/js/q_table/q_table_agent.js similarity index 96% rename from demo/webapp/public/js/q_table_agent.js rename to demo/webapp/public/js/q_table/q_table_agent.js index 9a96e22..e3ebc8d 100644 --- a/demo/webapp/public/js/q_table_agent.js +++ b/demo/webapp/public/js/q_table/q_table_agent.js @@ -54,7 +54,7 @@ class QTableAgent { play(){ // Get the current state - let state = this.env.getState(); + let state = this.env.getState().lidar; state = state.toString(); // In this state in not in the Q(s, a) function if (!(state in this.Q)){ @@ -108,7 +108,7 @@ class QTableAgent { console.log("episode=", ep, "eps=", eps, "mean_reward", mean(mean_reward)); } mean_reward = []; - let st = this.env.getState().toString(); + let st = this.env.getState().lidar.toString(); let act; let gamma = 0.99; let st2; @@ -117,7 +117,7 @@ class QTableAgent { act = this.pickAction(st, eps); let reward = this.env.step(act); mean_reward.push(reward); - st2 = this.env.getState().toString(); + st2 = this.env.getState().lidar.toString(); // Pick greedy action (eps = 0) act2 = this.pickAction(st2, 0.); this.createStateIfNotExist(st2); diff --git a/demo/webapp/public/js/traffic/index.js b/demo/webapp/public/js/traffic/index.js new file mode 100644 index 0000000..58531dd --- /dev/null +++ b/demo/webapp/public/js/traffic/index.js @@ -0,0 +1,67 @@ + +let levelUrl = metacar.level.level3; + +var env = new metacar.env("canvas", levelUrl); + +env.setAgentMotion(metacar.motion.ControlMotion, {}); +env.setAgentLidar({pts: 7, width: 3, height: 7, pos: -0.5}) + +// js/DDPG/ddpg.js +var agent = new DDPGAgent(env, { + stateSize: 50, + desiredActionStddev: 0.3, + initialStddev: 0.3 +}); + +initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); + +env.loop(() => { + let state = env.getState(); + displayState("realtime_viewer", state.lidar, 200, 200); + let reward = env.getLastReward(); + const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); + displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]); +}); + +env.load().then(() => { + + env.addEvent("train [Background]", () => { + let train = confirm("The training process takes some time and might slow this tab. Do you want to continue? \n You can also load a pre-trained model."); + if (train){ + env.render(false); + agent.train(false); + } + }); + + env.addEvent("Train [Show the training]", () => { + env.steping(false); + agent.train(true); + }); + + env.addEvent("shuffle", () => { + env.randomRoadPosition(); + }) + + env.addEvent("play", () => { + agent.play(); + }); + + env.addEvent("stop", () => { + agent.stop(); + }); + + env.addEvent("reset_env"); + + env.addEvent("save", () => { + agent.save("model-ddpg-traffic"); + }); + + env.addEvent("load", () => { + agent.restore("ddpg-traffic", "model-ddpg-traffic") + }); +}); + + + + + diff --git a/demo/webapp/public/models/ddpg/actor-model-ddpg-agent.json b/demo/webapp/public/models/ddpg-road/actor-model-ddpg-road.json similarity index 79% rename from demo/webapp/public/models/ddpg/actor-model-ddpg-agent.json rename to demo/webapp/public/models/ddpg-road/actor-model-ddpg-road.json index 3cc61fd..f5fb493 100644 --- a/demo/webapp/public/models/ddpg/actor-model-ddpg-agent.json +++ b/demo/webapp/public/models/ddpg-road/actor-model-ddpg-road.json @@ -1 +1 @@ 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zMiwyJ;Tp|d+yaG%U9iJs9tl;-#i+%fsa%gOs-Fien|z)+6jn8?zH<*J%~uBBIU6BR z`#X`UUyqklL(ywaGWPhb#1nlonY2ezpVsiSuXIbx*GQFo=kI9G~oV=4!X6f2s#tJ@#0QCrY=Gu8md_?+Q={~(vS_XqBZy$jEt9pk<2XhE}w(&%}7HIc2`0$EC8P%?I%$j$F1 z#{|X5$4h1OSY;3maYt(JK1NH0-;8zf~P#}v=-renGe7&+rFtyQ{5qelG5 z{>9@|tu`DCL?z(mEg8J`pEh&#_A;}SlqM=1R0KPkiz?tqArtXm{ubwA@ a{aSgf$gLQxtJ=c;i#Z1RK~6y3#NdD4a$&Oo From 44a2b2730489726707789d52cea26e848a4602e7 Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Wed, 27 Jun 2018 09:45:57 +0100 Subject: [PATCH 7/9] Little Refactoring + Models --- .../webapp/{level2.html => ddpg-traffic.html} | 32 +- demo/webapp/ddpg.html | 66 ++ demo/webapp/index.html | 55 +- demo/webapp/{level1.html => policy.html} | 9 +- demo/webapp/public/img/ddpg-traffic.png | Bin 0 -> 32222 bytes demo/webapp/public/img/level0.png | Bin 30964 -> 26321 bytes .../js/{traffic => ddpg-traffic}/index.js | 23 +- demo/webapp/public/js/ddpg/ddpg.js | 12 +- demo/webapp/public/js/ddpg/ddpg_agent.js | 78 +- demo/webapp/public/js/ddpg/index.js | 12 +- demo/webapp/public/js/ddpg/models.js | 20 + .../js/policy_monte_carlo/policy_agent.js | 2 +- .../webapp/public/js/q_table/q_table_agent.js | 2 +- .../actor-model-ddpg-traffic.json | 2 +- .../actor-model-ddpg-traffic.weights.bin | Bin 21640 -> 59656 bytes .../critic-model-ddpg-traffic.json | 2 +- .../critic-model-ddpg-traffic.weights.bin | Bin 22276 -> 60932 bytes demo/webapp/{level0.html => qtable.html} | 9 +- src/asset_manager.ts | 5 +- src/basic_motion_engine.ts | 3 + src/bot_motion_engine.ts | 1 + src/car.ts | 6 +- src/embedded.ts | 6 +- src/embedded/level/level_3.ts | 816 ++++++++++++++++++ src/level.ts | 2 +- src/metacar.ts | 39 +- src/world.ts | 18 + 27 files changed, 1118 insertions(+), 102 deletions(-) rename demo/webapp/{level2.html => ddpg-traffic.html} (55%) create mode 100644 demo/webapp/ddpg.html rename demo/webapp/{level1.html => policy.html} (88%) create mode 100644 demo/webapp/public/img/ddpg-traffic.png rename demo/webapp/public/js/{traffic => ddpg-traffic}/index.js (71%) rename demo/webapp/{level0.html => qtable.html} (89%) create mode 100644 src/embedded/level/level_3.ts diff --git a/demo/webapp/level2.html b/demo/webapp/ddpg-traffic.html similarity index 55% rename from demo/webapp/level2.html rename to demo/webapp/ddpg-traffic.html index d0c93ee..ff6108b 100644 --- a/demo/webapp/level2.html +++ b/demo/webapp/ddpg-traffic.html @@ -2,7 +2,7 @@ - Metacar: Full control + Metacar: Continous control in traffic @@ -24,22 +24,30 @@

Metacar

-

Current state (Lidar points)



+

+

Current state (Lidar points)




- You can use the arrow keys to control the car by yourself.

- - This level is not solved yet (working on it). However, You can contribute to the project by solving it and sharing your implementation!

+ The algorithm is based on the following papers: +

    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
  • Prioritized Experience Replay: paper
    • +
+
+ You can use the arrow keys to control the car by yourself.

The motion control is based on two continuous values for the throttle and steering angle of the car.

The left window gives you an overview of what the autonomous vehicle (in red) sees.

+ + You can find the code of this demo here and here.

+
- @@ -48,11 +56,11 @@

Current state (Lidar points)



- - - - - - + + + + + + diff --git a/demo/webapp/ddpg.html b/demo/webapp/ddpg.html new file mode 100644 index 0000000..1fa1e0a --- /dev/null +++ b/demo/webapp/ddpg.html @@ -0,0 +1,66 @@ + + + + + Metacar: Continous control + + + + + + +
+
+

Metacar

+ +
+
+ +
+ +
+
+ +
+

+

Current state (Lidar points)




+
+

+ The algorithm is based on the following papers: +

    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
  • Prioritized Experience Replay: paper
    • +
+
+ You can use the arrow keys to control the car by yourself.

+ + The motion control is based on two continuous values for the throttle and steering angle of the car. + +

The left window gives you an overview of what the autonomous vehicle (in red) sees.

+ + You can find the code of this demo here. +

+
+ +
+
+ + + + + + + + + + + + + + + + + diff --git a/demo/webapp/index.html b/demo/webapp/index.html index cec0729..09c30eb 100644 --- a/demo/webapp/index.html +++ b/demo/webapp/index.html @@ -37,45 +37,72 @@

Check out examples of algorithms created with metacar




- +

-
Q-learning with table +
Discrete Actions

+ Algorithm: Q-learning with table

In this example, the goal is to demonstrate a simple working example of a reinforcement learning algorithm using the metacar environment. You can check out - the demo or take a look at the medium tutorial. + the demo or take a look at the medium tutorial.

-
- +

-
Policy Monte carlo +
Discrete Actions in traffic

- Here is an axample of a policy gradient algorithm based on Monte Carlo exploration. - The neural network is build using tensorflow.js. Check out the demo. + Algorithm: Policy gradient algorithm based on Monte Carlo exploration (REINFORCE).

+ + The neural network is build using tensorflow.js. Check out the demo.

-
- +
+

-
Full control +
Continous Actions

+
+
    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
  • Prioritized Experience Replay: paper
    • +
+

- This level is not solved yet (working on it). However, you can contribute to the project by solving it and sharing your implementation!

-

+ The control is based on two continuous values for the throttle and steering angle of the car. + The model is made using tensorflow.js

+
+ +

+
Continous Actions in traffic +

+
+
    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
  • Prioritized Experience Replay: paper
    • +
+

+ The control is based on two continuous values for the throttle and steering angle of the car. + The model is made using tensorflow.js +

+

+
+ +
- +


Create your own level!

diff --git a/demo/webapp/level1.html b/demo/webapp/policy.html similarity index 88% rename from demo/webapp/level1.html rename to demo/webapp/policy.html index 9938b98..6f4a585 100644 --- a/demo/webapp/level1.html +++ b/demo/webapp/policy.html @@ -2,7 +2,7 @@ - Metacar: Policy gradient demo + Metacar: Discrete Actions in traffic. @@ -25,7 +25,7 @@

Metacar

-

Current state (Lidar points)



+

Current state (Lidar points)




Policy Gradient algorithm based on Monte Carlo exploration.
@@ -45,9 +45,10 @@

Current state (Lidar points)



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50, - desiredActionStddev: 0.3, - initialStddev: 0.3 + resetEpisode: true, + desiredActionStddev: 0.4, + initialStddev: 0.4, + actorFirstLayerSize: 128, + actorSecondLayerSize: 64, + criticFirstLayerSSize: 128, + criticFirstLayerASize: 128, + criticSecondLayerSize: 64, + nbEpochs: 1000 }); initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); +let it = 0; env.loop(() => { let state = env.getState(); displayState("realtime_viewer", state.lidar, 200, 200); let reward = env.getLastReward(); const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); - displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]); + if (it % 10 == 0) + displayScores("realtime_viewer", [qValue, state.a, state.steering], reward, ["Q(a, s)", "Acceleration", "Steering Angle"]); + it += 1; }); env.load().then(() => { @@ -38,10 +48,6 @@ env.load().then(() => { agent.train(true); }); - env.addEvent("shuffle", () => { - env.randomRoadPosition(); - }) - env.addEvent("play", () => { agent.play(); }); @@ -57,7 +63,8 @@ env.load().then(() => { }); env.addEvent("load", () => { - agent.restore("ddpg-traffic", "model-ddpg-traffic") + //agent.restore("ddpg-traffic", "model-ddpg-traffic") + agent.restore("four", "model-ddpg-traffic-epoch-120"); }); }); diff --git a/demo/webapp/public/js/ddpg/ddpg.js b/demo/webapp/public/js/ddpg/ddpg.js index c55c0a2..e8175c4 100644 --- a/demo/webapp/public/js/ddpg/ddpg.js +++ b/demo/webapp/public/js/ddpg/ddpg.js @@ -26,13 +26,13 @@ class DDPG { this.tfGamma = tf.scalar(config.gamma); // Inputs - let obsInput = tf.input({batchShape: [null, this.config.stateSize]}); - let actionInput = tf.input({batchShape: [null, this.config.nbActions]}); + this.obsInput = tf.input({batchShape: [null, this.config.stateSize]}); + this.actionInput = tf.input({batchShape: [null, this.config.nbActions]}); // Randomly Initialize actor network μ(s) - this.actor.buildModel(obsInput); + this.actor.buildModel(this.obsInput); // Randomly Initialize critic network Q(s, a) - this.critic.buildModel(obsInput, actionInput); + this.critic.buildModel(this.obsInput, this.actionInput); // Define in js/DDPG/models.js @@ -43,6 +43,10 @@ class DDPG { this.perturbedActor = copyModel(this.actor, Actor); //this.adaptivePerturbedActor = copyModel(this.actor, Actor); + this.setLearningOp(); + } + + setLearningOp(){ this.criticWithActor = (tfState) => { return tf.tidy(() => { const tfAct = this.actor.predict(tfState); diff --git a/demo/webapp/public/js/ddpg/ddpg_agent.js b/demo/webapp/public/js/ddpg/ddpg_agent.js index 0906222..bee45ee 100644 --- a/demo/webapp/public/js/ddpg/ddpg_agent.js +++ b/demo/webapp/public/js/ddpg/ddpg_agent.js @@ -36,8 +36,11 @@ class DDPGAgent { "criticSecondLayerSize": config.criticSecondLayerSize || 32, "maxStep": config.maxStep || 800, "stopOnRewardError": config.stopOnRewardError != undefined ? config.stopOnRewardError:true, - "resetEpisode": config.resetEpisode != undefined ? config.resetEpisode:false + "resetEpisode": config.resetEpisode != undefined ? config.resetEpisode:false, + "saveDuringTraining": config.saveDuringTraining || false, + "saveInterval": config.saveInterval || 20 }; + this.epoch = 0; // From js/DDPG/noise.js this.noise = new AdaptiveParamNoiseSpec(this.config); @@ -72,8 +75,20 @@ class DDPGAgent { /* Restore the weights of the network */ - this.ddpg.critic.model = await tf.loadModel('http://localhost:3000/public/models/'+folder+'/critic-'+name+'.json'); - this.ddpg.actor.model = await tf.loadModel("http://localhost:3000/public/models/"+folder+"/actor-"+name+".json"); + const critic = await tf.loadModel('http://localhost:3000/public/models/'+folder+'/critic-'+name+'.json'); + const actor = await tf.loadModel("http://localhost:3000/public/models/"+folder+"/actor-"+name+".json"); + + this.ddpg.critic = copyFromSave(critic, Critic, this.config, this.ddpg.obsInput, this.ddpg.actionInput); + this.ddpg.actor = copyFromSave(actor, Actor, this.config, this.ddpg.obsInput, this.ddpg.actionInput); + + // Define in js/DDPG/models.js + // Init target network Q' and μ' with the same weights + this.ddpg.actorTarget = copyModel(this.ddpg.actor, Actor); + this.ddpg.criticTarget = copyModel(this.ddpg.critic, Critic); + // Perturbed Actor (See parameter space noise Exploration paper) + this.ddpg.perturbedActor = copyModel(this.ddpg.actor, Actor); + //this.adaptivePerturbedActor = copyModel(this.actor, Actor); + this.ddpg.setLearningOp(); } /** @@ -132,25 +147,39 @@ class DDPGAgent { return {mDone, mState, tfState}; } + /** + * Optimize models and log states + */ + _optimize(){ + this.ddpg.noise.desiredActionStddev = Math.max(0.1, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev); + let lossValuesCritic = []; + let lossValuesActor = []; + console.time("Training"); + for (let t=0; t < this.config.nbTrainSteps; t++){ + let {lossC, lossA} = this.ddpg.optimizeCriticActor(); + lossValuesCritic.push(lossC); + lossValuesActor.push(lossA); + } + console.timeEnd("Training"); + console.log("desiredActionStddev:", this.ddpg.noise.desiredActionStddev); + setMetric("CriticLoss", mean(lossValuesCritic)); + setMetric("ActorLoss", mean(lossValuesActor)); + } + /** * Train DDPG Agent */ async train(realTime){ this.stopTraining = false; // One epoch - for (let e=0; e < this.config.nbEpochs; e++){ + for (this.epoch; this.epoch < this.config.nbEpochs; this.epoch++){ // Perform cycles. this.rewardsList = []; this.stepList = []; this.distanceList = []; - document.getElementById("trainingProgress").innerHTML = "Progression: "+e+"/"+this.config.nbEpochs+"
"; + document.getElementById("trainingProgress").innerHTML = "Progression: "+this.epoch+"/"+this.config.nbEpochs+"
"; for (let c=0; c < this.config.nbEpochsCycle; c++){ - if (c%10==0){ - logTfMemory(); - } - - // Perform rollouts. - // Get current observation + if (c%10==0){ logTfMemory(); } let mPreviousStep = this.env.getState().linear; let tfPreviousStep = tf.tensor2d([mPreviousStep]); let step = 0; @@ -178,29 +207,18 @@ class DDPGAgent { if (this.config.resetEpisode){ this.env.reset(); } - this.env.randomRoadPosition(); + this.env.shuffle({cars: false}); tfPreviousStep.dispose(); - console.log("e="+ e +", c="+c); + console.log("e="+ this.epoch +", c="+c); - //this.ddpg.targetUpdate(); await tf.nextFrame(); } - if (e > 5){ - this.ddpg.noise.desiredActionStddev = Math.max(0.1, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev); - let lossValuesCritic = []; - let lossValuesActor = []; - console.time("Training"); - for (let t=0; t < this.config.nbTrainSteps; t++){ - let {lossC, lossA} = this.ddpg.optimizeCriticActor(); - lossValuesCritic.push(lossC); - lossValuesActor.push(lossA); - } - console.timeEnd("Training"); - console.log("desiredActionStddev:", this.ddpg.noise.desiredActionStddev); - setMetric("CriticLoss", mean(lossValuesCritic)); - setMetric("ActorLoss", mean(lossValuesActor)); + if (this.epoch > 5){ + this._optimize(); + } + if (this.config.saveDuringTraining && this.epoch % this.config.saveInterval == 0 && this.epoch != 0){ + this.save("model-ddpg-traffic-epoch-"+this.epoch); } - setMetric("Reward", mean(this.rewardsList)); setMetric("EpisodeDuration", mean(this.stepList)); setMetric("NoiseDistance", mean(this.distanceList)); @@ -209,6 +227,6 @@ class DDPGAgent { this.env.render(true); - } + } }; \ No newline at end of file diff --git a/demo/webapp/public/js/ddpg/index.js b/demo/webapp/public/js/ddpg/index.js index 07e5927..cc998ec 100644 --- a/demo/webapp/public/js/ddpg/index.js +++ b/demo/webapp/public/js/ddpg/index.js @@ -2,24 +2,28 @@ let levelUrl = metacar.level.level2; var env = new metacar.env("canvas", levelUrl); - env.setAgentMotion(metacar.motion.ControlMotion, {}); env.setAgentLidar({pts: 5, width: 3, height: 7, pos: -0.5}) // js/DDPG/ddpg.js var agent = new DDPGAgent(env, { stateSize: 26, - resetEpisode: true + resetEpisode: true, + saveDuringTraining: true, + saveInterval: 10 }); initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); +let it = 0; env.loop(() => { let state = env.getState(); displayState("realtime_viewer", state.lidar, 200, 200); let reward = env.getLastReward(); const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); - displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]); + if (it % 10 == 0) + displayScores("realtime_viewer", [qValue, state.a, state.steering], reward, ["Q(a, s)", "Acceleration", "Steering Angle"]); + it += 1; }); env.load().then(() => { @@ -38,7 +42,7 @@ env.load().then(() => { }); env.addEvent("shuffle", () => { - env.randomRoadPosition(); + env.shuffle({cars: false}); }) env.addEvent("play", () => { diff --git a/demo/webapp/public/js/ddpg/models.js b/demo/webapp/public/js/ddpg/models.js index 7823acc..6ef1c3d 100644 --- a/demo/webapp/public/js/ddpg/models.js +++ b/demo/webapp/public/js/ddpg/models.js @@ -1,3 +1,23 @@ +/** + * Copy a model + * @param model Actor|Critic instance + * @param instance Actor|Critic + * @return Copy of the model + */ +function copyFromSave(model, instance, config, obs, action){ + return tf.tidy(() => { + nModel = new instance(config); + // action might be not required + nModel.buildModel(obs, action); + const weights = model.weights; + for (let m=0; m < weights.length; m++){ + nModel.model.weights[m].val.assign(weights[m].val); + } + return nModel; + }) +} + + /** * Copy a model * @param model Actor|Critic instance diff --git a/demo/webapp/public/js/policy_monte_carlo/policy_agent.js b/demo/webapp/public/js/policy_monte_carlo/policy_agent.js index b8c8bb6..5f2d2fd 100644 --- a/demo/webapp/public/js/policy_monte_carlo/policy_agent.js +++ b/demo/webapp/public/js/policy_monte_carlo/policy_agent.js @@ -300,7 +300,7 @@ class PolicyAgent { tf_advantages.dispose(); // Set the agent on a new free road - this.env.randomRoadPosition(); + this.env.shuffle({cars: false}); //env.reset(); // Go to the next episode setTimeout(() => this.train(this.env, it+1), 1); diff --git a/demo/webapp/public/js/q_table/q_table_agent.js b/demo/webapp/public/js/q_table/q_table_agent.js index e3ebc8d..58c532b 100644 --- a/demo/webapp/public/js/q_table/q_table_agent.js +++ b/demo/webapp/public/js/q_table/q_table_agent.js @@ -125,7 +125,7 @@ class QTableAgent { this.Q[st][act] = this.Q[st][act] + 0.01*(reward + (gamma*this.Q[st2][act2]) - this.Q[st][act]); st = st2; } - this.env.randomRoadPosition(); + this.env.shuffle({cars: false}); } this.env.render(true); for (let s=0; s < this.stateList.length; s++){ diff --git a/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic.json b/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic.json index 93a21c3..e44d5d4 100644 --- a/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic.json +++ b/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic.json @@ -1 +1 @@ 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zy&IYAa+)$ClPsGSCd$_rdJ#QV)2@K$fV+(7q@RmuvqTKGjM+}*M%{s>nPHgqqXzZ2 z<$=PN}n4*gQ|(#+GhYKzL<);oYwN}UN2T! z7|w#1-@$-@HF&i08(VBS4;sr{Fjrw4|F}Vh?~IzurrBxoGd(*>j?Ny-%681d^+^Xy zwzVPaFFHjw6nU|+MmG39r33vO1NgQgWqzoC8edg?05_Nk&dNx2dhDbk{TGqUVUK%mp_K^Lhv+E-;> zHKhoOl9HgrBNA+Dzv9kPbH0D?P - Metacar: Q-learning demo + Metacar: Discrete Actions. @@ -25,7 +25,7 @@

Metacar

-

Current state (Lidar points)


+

Current state (Lidar points)



Q-learning algorithm with table.
@@ -54,8 +54,9 @@

Q-table


- - + + + diff --git a/src/asset_manager.ts b/src/asset_manager.ts index c93474d..e7fa6f6 100644 --- a/src/asset_manager.ts +++ b/src/asset_manager.ts @@ -133,7 +133,7 @@ export class AssetManger { @line is Optional. 0 By default. */ if (line == undefined) - line = 0; + line = Math.min(1, Math.floor(Math.random() * 2)); let carById = this.level.findCarById(road.cars[0]); if (!force && road.cars.length >= 1 && carById && carById.core.line == line){ line = line == 0 ? 1:0; @@ -168,9 +168,6 @@ export class AssetManger { car.rotation = (th+line_side_factor_t); // Set the new road of the car car.checkAndsetNewRoad(road); - - - } createAsset(img: string, info: AssetInfo, textures: any, type: string){ diff --git a/src/basic_motion_engine.ts b/src/basic_motion_engine.ts index 3f148fd..ace5827 100644 --- a/src/basic_motion_engine.ts +++ b/src/basic_motion_engine.ts @@ -21,6 +21,7 @@ export class BasicMotionEngine extends MotionEngine { */ private rotationStep: number; private actions: string[]; + private maxSpeed: number = 1.0; constructor(level: Level, options: BasicMotionOptions) { super(level); @@ -49,6 +50,8 @@ export class BasicMotionEngine extends MotionEngine { this.setUpKeyboard(); // Setup up velocity to 0 this.car.v = 0; + this.car.a = 0; // Acceleration + this.car.yaw_rate = 0; this.detectInteractions(); } diff --git a/src/bot_motion_engine.ts b/src/bot_motion_engine.ts index af35ba4..1dfc9af 100644 --- a/src/bot_motion_engine.ts +++ b/src/bot_motion_engine.ts @@ -182,6 +182,7 @@ export class BotMotionEngine extends MotionEngine { let turn = false; let nx = this.car.mx + is_next_pos.mx; let ny = this.car.my + is_next_pos.my; + if (!this.isRoad(nx, ny)) { turn = true; this.car.optionalTurn = false; diff --git a/src/car.ts b/src/car.ts index a4a6309..5428fab 100644 --- a/src/car.ts +++ b/src/car.ts @@ -60,6 +60,8 @@ export interface CarSprite extends PIXI.Sprite { yaw_rate?: number; last_a?: number; last_yaw_rate?: number; + next_road?: any; + turnedRandom?: any; } export interface LidarChild extends PIXI.Graphics { @@ -83,7 +85,6 @@ export class Car { public motion: any; private info: CarInfo; - public turnedRandom: any; constructor(level: Level|Editor, info: CarInfo, textures: any, options:CarOptions={}) { /* @@ -175,7 +176,8 @@ export class Car { // TOdo: CHECK something is strange here this.core.haveTurned = false; this.core.optionalTurn = false; - this.turnedRandom = undefined; + this.core.turnedRandom = undefined; + this.core.next_road = false; if (n_road) n_road.cars.push(this.core.carId); } diff --git a/src/embedded.ts b/src/embedded.ts index b315f19..a2ea369 100644 --- a/src/embedded.ts +++ b/src/embedded.ts @@ -2,6 +2,7 @@ import {fullCity} from "./embedded/level/full_city"; import {level1} from "./embedded/level/level_1"; import {level0} from "./embedded/level/level_0"; import {level2} from "./embedded/level/level_2"; +import {level3} from "./embedded/level/level_3"; /** * Object used to enumerate each @@ -17,6 +18,7 @@ export interface embeddedUrlI { fullCity: string; level1: string; level2: string; + level3: string; level0: string; }; @@ -24,6 +26,7 @@ export const embeddedUrl: embeddedUrlI = { fullCity: "embedded://level/fullCity", level1: "embedded://level/level1", level2: "embedded://level/level2", + level3: "embedded://level/level3", level0: "embedded://level/level0", } @@ -32,6 +35,7 @@ export const embeddedContent: any = { fullCity: fullCity, level1: level1, level0: level0, - level2: level2 + level2: level2, + level3: level3 } } \ No newline at end of file diff --git a/src/embedded/level/level_3.ts b/src/embedded/level/level_3.ts new file mode 100644 index 0000000..5b5447b --- /dev/null +++ b/src/embedded/level/level_3.ts @@ -0,0 +1,816 @@ +export const level3: any = { + "cars": [ + { + "mx": 1, + "my": 3, + "line": 0 + }, + { + "mx": 1, + "my": 8, + "line": 0 + }, + { + "mx": 4, + "my": 8, + "line": 0 + }, + { + "mx": 7, + "my": 7, + "line": 1 + }, + { + "mx": 9, + "my": 6, + "line": 0 + }, + { + "mx": 11, + "my": 4, + "line": 1 + }, + { + "mx": 9, + "my": 3, + "line": 1 + }, + { + "mx": 7, + "my": 2, + "line": 1 + }, + { + "mx": 5, + "my": 1, + "line": 1 + }, + { + "mx": 2, + "my": 1, + "line": 1 + }, + { + "mx": 6, + "my": 8, + "line": 0 + }, + { + "mx": 1, + "my": 6, + "line": 0 + }, + { + "mx": 5, + "my": 5, + "line": 1 + }, + { + "mx": 4, + "my": 3, + "line": 1 + }, + { + "mx": 3, + "my": 4, + "line": 0 + }, + { + "mx": 4, + "my": 6, + "line": 0 + } + ], + "road": [ + { + "x": 120, + "y": 60 + }, + { + "x": 180, + "y": 60 + }, + { + "x": 240, + "y": 60 + }, + { + "x": 60, + "y": 60 + }, + { + "x": 60, + "y": 120 + }, + { + "x": 60, + "y": 180 + }, + { + "x": 60, + "y": 240 + }, + { + "x": 60, + "y": 300 + }, + { + "x": 60, + "y": 360 + }, + { + "x": 60, + "y": 420 + }, + { + "x": 60, + "y": 480 + }, + { + "x": 120, + "y": 480 + }, + { + "x": 180, + "y": 480 + }, + { + "x": 240, + "y": 480 + }, + { + "x": 300, + "y": 60 + }, + { + "x": 300, + "y": 120 + }, + { + "x": 300, + 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diff --git a/src/level.ts b/src/level.ts index f7f12f2..8daaaea 100644 --- a/src/level.ts +++ b/src/level.ts @@ -105,7 +105,7 @@ export class Level extends World { */ this.agent.reset(); for (var c = 0; c < this.cars.length; c++) { - //this.cars[c].reset(); + this.cars[c].reset(); } } diff --git a/src/metacar.ts b/src/metacar.ts index e3a32bc..38480fe 100644 --- a/src/metacar.ts +++ b/src/metacar.ts @@ -18,6 +18,15 @@ export interface eventLoadOptions { local: boolean; } +/** + * @cars Shuffle the position of the other cars (true default) + * @agent Shuffle the position of the agent (true default) + */ +export interface shuffleConfig{ + agent: boolean; + cars: boolean; +} + export class MetaCar { private level: Level; @@ -189,22 +198,32 @@ export class MetaCar { } /** - * Set the agent on a new random road on the map. + * Shuffle the position of the agent and the others + * cars. */ - randomRoadPosition(): void{ + shuffle(config: shuffleConfig): void{ /* This position */ - let roads = this.level.getRoads(); - let keys = Object.keys(roads); - keys.sort(function() {return Math.random()-0.5;}); - for (let k in keys){ - let road = roads[keys[k]]; - if (road.cars.length == 0){ - road.setCarPosition(this.level.agent.core); - break; + config = config || {cars: true, agent: true}; + config.cars = config.cars != undefined ? config.cars:true; + config.agent = config.agent != undefined ? config.agent:true; + + if (config.agent) { + let roads = this.level.getRoads(); + let keys = Object.keys(roads); + keys.sort(function() {return Math.random()-0.5;}); + for (let k in keys){ + let road = roads[keys[k]]; + if (road.cars.length == 0){ + road.setCarPosition(this.level.agent.core); + break; + } } } + if (config.cars){ + this.level.shuffleCarsPositions(); + } } /** diff --git a/src/world.ts b/src/world.ts index 0d76078..1bfe47d 100644 --- a/src/world.ts +++ b/src/world.ts @@ -153,4 +153,22 @@ export class World { public setSteping(val: boolean): void{ this.steping = val; } + + public shuffleCarsPositions(){ + for (let c = 0; c < this.cars.length; c++){ + + let roads = this.roads; + let keys = Object.keys(roads); + keys.sort(function() {return Math.random()-0.5;}); + for (let k in keys){ + let road = roads[keys[k]]; + if (road.cars.length == 0){ + road.setCarPosition(this.cars[c].core); + break; + } + } + + } + } + } \ No newline at end of file From 2459bc19ad896267ab2379c50e3d281461afe2a7 Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Wed, 27 Jun 2018 10:58:22 +0100 Subject: [PATCH 8/9] Save Interval --- demo/webapp/public/js/ddpg/index.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/webapp/public/js/ddpg/index.js b/demo/webapp/public/js/ddpg/index.js index cc998ec..984feae 100644 --- a/demo/webapp/public/js/ddpg/index.js +++ b/demo/webapp/public/js/ddpg/index.js @@ -10,7 +10,7 @@ var agent = new DDPGAgent(env, { stateSize: 26, resetEpisode: true, saveDuringTraining: true, - saveInterval: 10 + saveInterval: 10, }); initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); From f1f91b64d8df205468a368d35871d43ae595fb0e Mon Sep 17 00:00:00 2001 From: Thibault Neveu Date: Wed, 27 Jun 2018 14:11:53 +0100 Subject: [PATCH 9/9] DDPG finished --- demo/dist/metacar.min.js | 2 +- demo/webapp/ddpg-traffic.html | 2 +- demo/webapp/ddpg.html | 2 +- demo/webapp/index.html | 2 - demo/webapp/public/js/ddpg-traffic/index.js | 3 +- demo/webapp/public/js/ddpg/ddpg.js | 1 + demo/webapp/public/js/ddpg/ddpg_agent.js | 8 +- demo/webapp/public/js/ddpg/index.js | 5 +- demo/webapp/public/js/ddpg/memory.js | 237 +++-------------- .../public/js/ddpg/prioritized_memory.js | 240 ++++++++++++++++++ .../actor-model-ddpg-traffic-epoch-120.json | 1 + ...r-model-ddpg-traffic-epoch-120.weights.bin | Bin 0 -> 59656 bytes .../actor-model-ddpg-traffic.json | 1 - .../actor-model-ddpg-traffic.weights.bin | Bin 59656 -> 0 bytes .../critic-model-ddpg-traffic-epoch-120.json | 1 + ...c-model-ddpg-traffic-epoch-120.weights.bin | Bin 0 -> 60932 bytes .../critic-model-ddpg-traffic.json | 1 - .../critic-model-ddpg-traffic.weights.bin | Bin 60932 -> 0 bytes dist/metacar.min.js | 2 +- 19 files changed, 293 insertions(+), 215 deletions(-) create mode 100644 demo/webapp/public/js/ddpg/prioritized_memory.js create mode 100644 demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.json create mode 100644 demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.weights.bin delete mode 100644 demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic.json delete mode 100644 demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic.weights.bin create mode 100644 demo/webapp/public/models/ddpg-traffic/critic-model-ddpg-traffic-epoch-120.json create mode 100644 demo/webapp/public/models/ddpg-traffic/critic-model-ddpg-traffic-epoch-120.weights.bin delete mode 100644 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0,this.core.carId=C,C+=1,this.core.agent=!1,this.core.obstacle=!0,this.core.mapId=u.CAR,o.lidar&&(this.createLidar(o.lidarInfo),this.core.lidar=this.lidar),o.motionEngine?(this.motion=o.motionEngine,this.motion.setUp(this.core,this.lidar),this.core.rotationStep=.5):this.core.rotationStep=.5,this.core.mx=this.info.mx,this.core.my=this.info.my,this.core.x=this.info.mx*d,this.core.y=this.info.my*d,this.core.checkAndsetNewRoad=function(t){return n.checkAndsetNewRoad(t)};var r=this.level.getRoad(this.core.my,this.core.mx);this.core.line=0,r&&r.setCarPosition(this.core,this.info.line)}return t.prototype.checkAndsetNewRoad=function(t){var e=this.core.road;t||(t=this.level.getRoad(this.core.my,this.core.mx)),t!=e&&(e&&e.cars.splice(e.cars.indexOf(this.core.carId),1),this.core.road=t,this.core.haveTurned=!1,this.core.optionalTurn=!1,this.turnedRandom=void 0,t&&t.cars.push(this.core.carId))},t.prototype.reset=function(){this.core.mx=this.info.mx,this.core.my=this.info.my;var t=this.level.getRoad(this.core.my,this.core.mx);t&&t.setCarPosition(this.core,this.info.line,!0)},t.prototype.getState=function(t){if(void 0===t&&(t=!1),t){var e=[];return this.motion.state.map(function(t){e=e.concat(t)}),e}return this.motion.state.map(function(t){return t.slice()})},t.prototype.step=function(t,e){if(void 0===e&&(e=null),null==e)var i=this.motion.step(t),o=i.agentCollisions,n=i.onRoad;else{var a=this.motion.actionStep(t,e);o=a.agentCollisions,n=a.onRoad}return{agentCollisions:o,onRoad:n}},t.prototype.createLidar=function(t){t.pts=t.pts||5,t.width=t.width||4,t.height=t.height||5,t.pos=t.pos||0,this.lidar=new s,this.lidar.lidarPts=[],this.lidar.collisionPts=[];var e=new r;e.pt=!1,e.alpha=.3,e.beginFill(5329233),e.drawRect(0,0,this.core.width*t.width,this.core.height*t.height),e.endFill();var i=new r;i.pt=!1,i.alpha=0,i.beginFill(5308416),i.drawRect(this.core.width-5,e.height/2,3,3),i.endFill();var o=new r;o.pt=!1,o.alpha=0,o.beginFill(5308416),o.drawRect(-1,e.height/2-2,3,3),o.endFill();var n=new r;n.pt=!1,n.alpha=0,n.beginFill(5308416),n.drawRect(this.core.width/2,e.height/2-this.core.height/2+5,3,3),n.endFill();var a=new r;a.pt=!1,a.alpha=0,a.beginFill(5308416),a.drawRect(this.core.width/2,e.height/2+this.core.height/2-8,3,3),a.endFill(),o.x-=t.pos*this.core.width,i.x-=t.pos*this.core.width,a.x-=t.pos*this.core.width,n.x-=t.pos*this.core.width;for(var h=e.width/t.pts,c=e.height/t.pts,l=t.pts-1;l>=0;l--)for(var d=0;do.x&&i.xo.y&&i.y=4||!t)&&(r=!0),{agentCollisions:o,onRoad:r}},t.prototype.setState=function(t){for(var e=0,i=0;ithis.state[o][n]||t[a].mapId==u.ROAD&&this.state[o][n]==u.DEFAULT)&&(this.state[o][n]=t[a].mapId)}e+=1}},t}(),b=(I=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])},function(t,e){function i(){this.constructor=t}I(t,e),t.prototype=null===e?Object.create(e):(i.prototype=e.prototype,new i)}),R=function(t){function e(e,i){var o=t.call(this,e)||this;return o.rotationStep=i.rotationStep||.5,o.actions=i.actions||["UP","LEFT","RIGHT","DOWN","WAIT"],o}return b(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i0&&(this.car.v=0,this.car.vy=0),{agentCollisions:i,onRoad:o}},e}(M),E=function(){var t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])};return function(e,i){function o(){this.constructor=e}t(e,i),e.prototype=null===i?Object.create(i):(o.prototype=i.prototype,new o)}}(),L=function(t){function e(e){var i=t.call(this,e)||this;return i.map=i.level.getMap(),i.mapSizeY=i.map.length,i.mapSizeX=i.map[0].length,i.rotationStep=.5,i.rotationToNextCase={},i.rotationToNextCase[0]={mx:1,my:0},i.rotationToNextCase[.5]={mx:0,my:1},i.rotationToNextCase[1]={mx:-1,my:0},i.rotationToNextCase[1.5]={mx:0,my:-1},i.rotationToNextCase[-.5]={mx:0,my:-1},i.rotationToNextCase[-1]={mx:-1,my:0},i.rotationToNextCase[-1.5]={mx:0,my:1},i}return E(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i=this.mapSizeY||t<0||t>=this.mapSizeX||0==this.map[e][t])return!1;if(i&&this.level.getRoad(e,t).cars.length>0)return!1;return!0},e.prototype.autoRotation=function(){var t=d+d/3,e=d-d/3.5,i=d;if(this.car.next_road){var o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var a=this.car.rotation/Math.PI,r=(this.car.rotation/Math.PI+.5)%2,s=(this.car.rotation/Math.PI-.5)%2,h=this.rotationToNextCase[a],c=this.rotationToNextCase[r],l=this.rotationToNextCase[s],p=!1,u=this.car.mx+h.mx,v=this.car.my+h.my;if(this.isRoad(u,v)?this.car.haveTurned||this.car.turnedRandom?!this.car.haveTurned&&this.car.turnedRandom<.5&&this.isRoad(this.car.mx+c.mx,this.car.my+c.my)?(p=!0,this.car.optionalTurn=!0):!this.car.haveTurned&&this.car.turnedRandom>=.5&&this.isRoad(this.car.mx+l.mx,this.car.my+l.my)&&(p=!0,this.car.optionalTurn=!0):this.car.turnedRandom=Math.random():(p=!0,this.car.optionalTurn=!1),p)for(var y=[(a+.5)%2,(a-.5)%2,null],m=0;m=1&&n&&n.core.line==i&&(i=0==i?1:0),e.line=i,e.x=t.x,e.y=t.y,e.x+=d/2,e.y+=d/2;var a=1*d/4;t.orientation=Math.floor(t.orientation/e.rotationStep)*e.rotationStep;var r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e){void 0===e&&(e=null);for(var i=0;i0&&0==this.car.a?this.car.v=Math.max(0,this.car.v-.01):this.car.v<0&&0==this.car.a&&(this.car.v=Math.min(0,this.car.v+.01)),this.car.a>0&&this.car.v>=0?this.car.v=Math.min(1.5,this.car.v+.01*this.car.a):this.car.a>0&&this.car.v<0&&(this.car.v=Math.min(1.5,this.car.v+.03*this.car.a)),this.car.a<0&&this.car.v<=0?this.car.v=Math.max(-1.5,this.car.v+.01*this.car.a):this.car.a<0&&this.car.v>0&&(this.car.v=Math.max(-1.5,this.car.v+.03*this.car.a)),0==this.car.yaw_rate)this.car.x+=this.car.v*Math.cos(this.car.rotation)*t,this.car.y+=this.car.v*Math.sin(this.car.rotation)*t;else{var e=.01*this.car.yaw_rate*(this.car.v+.001)*Math.PI;this.car.x+=this.car.v/e*(Math.sin(this.car.rotation+e*t)-Math.sin(this.car.rotation)),this.car.y+=this.car.v/e*(Math.cos(this.car.rotation)-Math.cos(this.car.rotation+e*t)),this.car.rotation+=e*t}this.car.mx=Math.floor(this.car.x/d),this.car.my=Math.floor(this.car.y/d),this.car.checkAndsetNewRoad(),this.lidar.x=this.car.x,this.lidar.y=this.car.y,this.lidar.rotation=this.car.rotation;var i=this.detectInteractions(!0,!0),o=i.agentCollisions,n=i.onRoad;return o.length>0&&(this.car.v=0,this.car.vy=0),{agentCollisions:o,onRoad:n}},e}(M),j={env:k,editor:U,level:{fullCity:"embedded://level/fullCity",level1:"embedded://level/level1",level2:"embedded://level/level2",level0:"embedded://level/level0"},motion:{BasicMotion:R,ControlMotion:B}};e.default=j}]).default; \ No newline at end of file +window.metacar=function(t){var e={};function i(o){if(e[o])return e[o].exports;var n=e[o]={i:o,l:!1,exports:{}};return t[o].call(n.exports,n,n.exports,i),n.l=!0,n.exports}return i.m=t,i.c=e,i.d=function(t,e,o){i.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:o})},i.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},i.t=function(t,e){if(1&e&&(t=i(t)),8&e)return t;if(4&e&&"object"==typeof t&&t&&t.__esModule)return t;var o=Object.create(null);if(i.r(o),Object.defineProperty(o,"default",{enumerable:!0,value:t}),2&e&&"string"!=typeof t)for(var n in t)i.d(o,n,function(e){return t[e]}.bind(null,n));return o},i.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return i.d(e,"a",e),e},i.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},i.p="",i(i.s=1)}([function(t,e){t.exports=window.PIXI},function(t,e,i){"use strict";i.r(e);var o=i(0),n=(o.Application,o.loader),a=(o.loader.resources,o.Sprite),r=o.Graphics,s=o.Container,h="https://metacar-project.com/",c=h+"public/textures/textures.json",l=h+"public/images/",d=60,p={DEFAULT:"car.png",AGENT:"car_agent.png"},y={ROAD:-1,DEFAULT:0,CAR:1,AGENT:1},u={ROADS:{"↕":{image:"road_u.png",orientation:1.5},"↔":{image:"road_r.png",orientation:0},"↱":{image:"road_rotate_ld.png",orientation:1.25},"↰":{image:"road_rotate_ul.png",orientation:.75},"↲":{image:"road_rotate_rl.png",orientation:.25},"↳":{image:"road_rotate_dl.png",orientation:1.75},"↟":{image:"road_up.png",orientation:1.5},"↠":{image:"road_rp.png",orientation:0},"↤":{image:"road_r2.png",orientation:0},"↦":{image:"road_l2.png",orientation:1},"↥":{image:"road_d2.png",orientation:.5},"↧":{image:"road_u2.png",orientation:1.5}},house:{image:"house.png"},house2:{image:"house2.png"},house3:{image:"house3.png"},bench:{image:"bench.png"},tree:{image:"tree.png"}},x={level:{fullCity:{cars:[{mx:2,my:5,line:0,auto:!0},{mx:3,my:4,line:0,auto:!0},{mx:3,my:4,line:1,auto:!0},{mx:6,my:3,line:0,auto:!0},{mx:6,my:3,line:1,auto:!0},{mx:8,my:2,line:0,auto:!0},{mx:8,my:2,line:1,auto:!0},{mx:2,my:7,line:0,auto:!0}],house:[{x:9,y:409},{x:146,y:162},{x:439,y:39}],house2:[{x:425,y:192},{x:431,y:354}],house3:[{x:426,y:281},{x:51,y:157},{x:553,y:34}],bench:[{x:259,y:206},{x:8,y:205}],tree:[{x:1,y:303},{x:54,y:357},{x:215,y:325},{x:230,y:400},{x:282,y:368},{x:182,y:433},{x:285,y:309},{x:576,y:233},{x:581,y:300},{x:519,y:388},{x:582,y:391},{x:256,y:69},{x:151,y:43},{x:77,y:77},{x:26,y:24},{x:189,y:98}],map:[[0,0,0,0,0,0,"↕",0,0,0,0],[0,0,0,0,0,0,"↟",0,0,0,0],[0,0,0,0,0,0,"↦","↔","↔","↠","↔"],[0,0,0,0,0,0,"↕",0,0,0,0],["↔","↔","↧","↔","↠","↔","↤",0,0,0,0],[0,0,"↕",0,0,0,"↕",0,0,0,0],[0,0,"↟",0,0,0,"↟",0,0,0,0],[0,0,"↕",0,0,0,"↕",0,0,0,0]]},level1:{cars:[{mx:5,my:3,line:0,auto:!0},{mx:5,my:3,line:1,auto:!0},{mx:5,my:1,line:0,auto:!0}],tree:[{x:120,y:50},{x:86.5,y:85},{x:94.5,y:135},{x:30.5,y:128},{x:72.5,y:190},{x:23.5,y:232},{x:110.5,y:242},{x:43.5,y:33},{x:18.5,y:178},{x:305.5,y:125},{x:360.5,y:122}],asset:[{x:252.5,y:142},{x:86.5,y:85},{x:94.5,y:135},{x:30.5,y:128},{x:252.5,y:26},{x:195.5,y:0},{x:307.5,y:1},{x:72.5,y:190},{x:23.5,y:232},{x:110.5,y:242},{x:43.5,y:33},{x:35.5,y:185},{x:18.5,y:178},{x:289.5,y:126},{x:337.5,y:126},{x:305.5,y:125},{x:360.5,y:122},{x:492.5,y:56},{x:503.5,y:136},{x:504.5,y:58}],bench:[{x:252.5,y:142},{x:252.5,y:26}],road:[{x:240,y:60},{x:240,y:60},{x:480,y:0},{x:480,y:60},{x:480,y:120},{x:480,y:180},{x:480,y:240},{x:300,y:180},{x:300,y:180},{x:360,y:180},{x:420,y:180},{x:420,y:120},{x:420,y:60},{x:360,y:60},{x:300,y:60},{x:420,y:120}],house3:[{x:503.5,y:136}],house:[{x:504.5,y:58}],map:[[0,0,0,0,0,0,0,0,0,0],[0,0,0,"↱","↠","↔","↔","↰",0,0],[0,0,0,"↕",0,0,0,"↟",0,0],[0,0,0,"↳","↔","↔","↔","↲",0,0],[0,0,0,0,0,0,0,0,0,0]],agent:{mx:4,my:3,line:0,motion:{type:"BasicMotionEngine",options:{rotationStep:.5,actions:["UP","LEFT","RIGHT"]}}}},level0:{cars:[],house:[{x:263,y:108},{x:112,y:258}],house3:[{x:262,y:16}],bench:[{x:135,y:34}],tree:[{x:124,y:134},{x:7,y:11},{x:69,y:10},{x:19,y:238},{x:5,y:133},{x:260,y:211}],map:[[0,0,0,0,0],[0,"↱","↔","↰",0],[0,"↕",0,"↕",0],[0,"↳","↠","↲",0],[0,0,0,0,0]],agent:{mx:2,my:1,line:1,motion:{type:"BasicMotionEngine",options:{rotationStep:.5,actions:["UP","LEFT","RIGHT","DOWN","WAIT"]}}}},level2:{cars:[],road:[{x:300,y:360},{x:300,y:360},{x:360,y:360},{x:420,y:360},{x:240,y:360},{x:180,y:360},{x:120,y:360},{x:480,y:360},{x:480,y:360},{x:120,y:360},{x:60,y:360},{x:300,y:300},{x:300,y:240},{x:480,y:360},{x:480,y:360},{x:540,y:360},{x:300,y:180},{x:360,y:180},{x:240,y:180},{x:240,y:180},{x:180,y:180},{x:420,y:180},{x:420,y:120},{x:180,y:120},{x:300,y:60},{x:420,y:60},{x:180,y:60},{x:240,y:60},{x:360,y:60},{x:480,y:60},{x:120,y:60},{x:540,y:60},{x:540,y:120},{x:540,y:180},{x:540,y:240},{x:540,y:300},{x:60,y:60},{x:60,y:120},{x:60,y:180},{x:60,y:240},{x:60,y:300},{x:60,y:240},{x:360,y:180},{x:480,y:360}],asset:[{x:162,y:264},{x:407,y:264},{x:259,y:126},{x:306,y:33},{x:491,y:148},{x:391,y:431},{x:608,y:35},{x:484,y:224},{x:248,y:297},{x:376,y:336},{x:78,y:429},{x:1,y:205},{x:3,y:106},{x:-46,y:290},{x:622,y:154},{x:617,y:280}],house:[{x:162,y:264},{x:622,y:154}],house3:[{x:407,y:264},{x:-46,y:290}],tree:[{x:259,y:126},{x:491,y:148},{x:391,y:431},{x:608,y:35},{x:484,y:224},{x:248,y:297},{x:78,y:429},{x:1,y:205},{x:3,y:106},{x:617,y:280}],bench:[{x:306,y:33},{x:376,y:336}],map:[[0,0,0,0,0,0,0,0,0,0,0],[0,"↱","↔","↧","↔","↔","↔","↧","↔","↰",0],[0,"↕",0,"↕",0,0,0,"↕",0,"↕",0],[0,"↕",0,"↳","↔","↧","↠","↲",0,"↕",0],[0,"↟",0,0,0,"↕",0,0,0,"↕",0],[0,"↕",0,0,0,"↕",0,0,0,"↕",0],[0,"↳","↔","↔","↔","↥","↔","↔","↠","↲",0],[0,0,0,0,0,0,0,0,0,0,0]],agent:{mx:4,my:3,line:0,motion:{type:"BasicMotionEngine",options:{rotationStep:.5,actions:["UP","LEFT","RIGHT","DOWN","WAIT"]}}}},level3:{cars:[{mx:1,my:3,line:0},{mx:1,my:8,line:0},{mx:4,my:8,line:0},{mx:7,my:7,line:1},{mx:9,my:6,line:0},{mx:11,my:4,line:1},{mx:9,my:3,line:1},{mx:7,my:2,line: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0,this.core.carId=C,C+=1,this.core.agent=!1,this.core.obstacle=!0,this.core.mapId=y.CAR,o.lidar&&(this.createLidar(o.lidarInfo),this.core.lidar=this.lidar),o.motionEngine?(this.motion=o.motionEngine,this.motion.setUp(this.core,this.lidar),this.core.rotationStep=.5):this.core.rotationStep=.5,this.core.mx=this.info.mx,this.core.my=this.info.my,this.core.x=this.info.mx*d,this.core.y=this.info.my*d,this.core.checkAndsetNewRoad=function(t){return n.checkAndsetNewRoad(t)};var r=this.level.getRoad(this.core.my,this.core.mx);this.core.line=0,r&&r.setCarPosition(this.core,this.info.line)}return t.prototype.checkAndsetNewRoad=function(t){var e=this.core.road;t||(t=this.level.getRoad(this.core.my,this.core.mx)),t!=e&&(e&&e.cars.splice(e.cars.indexOf(this.core.carId),1),this.core.road=t,this.core.haveTurned=!1,this.core.optionalTurn=!1,this.core.turnedRandom=void 0,this.core.next_road=!1,t&&t.cars.push(this.core.carId))},t.prototype.reset=function(){this.core.mx=this.info.mx,this.core.my=this.info.my;var t=this.level.getRoad(this.core.my,this.core.mx);t&&t.setCarPosition(this.core,this.info.line,!0)},t.prototype.getState=function(){var t={},e=[];return this.motion.state.map(function(t){e=e.concat(t)}),e.push(this.core.v),t.linear=e,t.lidar=this.motion.state.map(function(t){return t.slice()}),t.v=this.core.v,t.a=this.core.last_a,t.steering=this.core.last_yaw_rate,t},t.prototype.step=function(t,e){if(void 0===e&&(e=null),null==e)var i=this.motion.step(t),o=i.agentCollisions,n=i.onRoad;else{var a=this.motion.actionStep(t,e);o=a.agentCollisions,n=a.onRoad}return{agentCollisions:o,onRoad:n}},t.prototype.createLidar=function(t){t.pts=t.pts||5,t.width=t.width||4,t.height=t.height||5,t.pos=t.pos||0,this.lidar=new s,this.lidar.lidarPts=[],this.lidar.collisionPts=[];var e=new r;e.pt=!1,e.alpha=.3,e.beginFill(5329233),e.drawRect(0,0,this.core.width*t.width,this.core.height*t.height),e.endFill();var i=new r;i.pt=!1,i.alpha=0,i.beginFill(5308416),i.drawRect(this.core.width-5,e.height/2,3,3),i.endFill();var o=new r;o.pt=!1,o.alpha=0,o.beginFill(5308416),o.drawRect(-1,e.height/2-2,3,3),o.endFill();var n=new r;n.pt=!1,n.alpha=0,n.beginFill(5308416),n.drawRect(this.core.width/2,e.height/2-this.core.height/2+5,3,3),n.endFill();var a=new r;a.pt=!1,a.alpha=0,a.beginFill(5308416),a.drawRect(this.core.width/2,e.height/2+this.core.height/2-8,3,3),a.endFill(),o.x-=t.pos*this.core.width,i.x-=t.pos*this.core.width,a.x-=t.pos*this.core.width,n.x-=t.pos*this.core.width;for(var h=e.width/t.pts,c=e.height/t.pts,l=t.pts-1;l>=0;l--)for(var d=0;do.x&&i.xo.y&&i.y=4||!t)&&(r=!0),{agentCollisions:o,onRoad:r}},t.prototype.setState=function(t){for(var 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o(){this.constructor=e}t(e,i),e.prototype=null===i?Object.create(i):(o.prototype=i.prototype,new o)}}(),S=function(t){function e(e){var i=t.call(this,e)||this;return i.map=i.level.getMap(),i.mapSizeY=i.map.length,i.mapSizeX=i.map[0].length,i.rotationStep=.5,i.rotationToNextCase={},i.rotationToNextCase[0]={mx:1,my:0},i.rotationToNextCase[.5]={mx:0,my:1},i.rotationToNextCase[1]={mx:-1,my:0},i.rotationToNextCase[1.5]={mx:0,my:-1},i.rotationToNextCase[-.5]={mx:0,my:-1},i.rotationToNextCase[-1]={mx:-1,my:0},i.rotationToNextCase[-1.5]={mx:0,my:1},i}return E(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i=this.mapSizeY||t<0||t>=this.mapSizeX||0==this.map[e][t])return!1;if(i&&this.level.getRoad(e,t).cars.length>0)return!1;return!0},e.prototype.autoRotation=function(){var t=d+d/3,e=d-d/3.5,i=d;if(this.car.next_road){var o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var a=this.car.rotation/Math.PI,r=(this.car.rotation/Math.PI+.5)%2,s=(this.car.rotation/Math.PI-.5)%2,h=this.rotationToNextCase[a],c=this.rotationToNextCase[r],l=this.rotationToNextCase[s],p=!1,y=this.car.mx+h.mx,u=this.car.my+h.my;if(this.isRoad(y,u)?this.car.haveTurned||this.car.turnedRandom?!this.car.haveTurned&&this.car.turnedRandom<.5&&this.isRoad(this.car.mx+c.mx,this.car.my+c.my)?(p=!0,this.car.optionalTurn=!0):!this.car.haveTurned&&this.car.turnedRandom>=.5&&this.isRoad(this.car.mx+l.mx,this.car.my+l.my)&&(p=!0,this.car.optionalTurn=!0):this.car.turnedRandom=Math.random():(p=!0,this.car.optionalTurn=!1),p)for(var x=[(a+.5)%2,(a-.5)%2,null],v=0;v=1&&n&&n.core.line==i&&(i=0==i?1:0),e.line=i,e.a=0,e.v=0,e.x=t.x,e.y=t.y,e.x+=d/2,e.y+=d/2;var a=1*d/4;t.orientation=Math.floor(t.orientation/e.rotationStep)*e.rotationStep;var r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e,i){if(void 0===e&&(e=null),void 0===i&&(i=!0),!i||this.steping){for(var o=0;o0&&0==this.car.a?this.car.v=Math.max(0,this.car.v-.01):this.car.v<0&&0==this.car.a&&(this.car.v=Math.min(0,this.car.v+.01)),this.car.a>0&&this.car.v>=0?this.car.v=Math.min(this.maxSpeed,this.car.v+.005*this.car.a):this.car.a>0&&this.car.v<0&&(this.car.v=Math.min(this.maxSpeed,this.car.v+.03*this.car.a)),this.car.a<0&&this.car.v<=0?this.car.v=Math.max(-this.maxSpeed,this.car.v+.005*this.car.a):this.car.a<0&&this.car.v>0&&(this.car.v=Math.max(-this.maxSpeed,this.car.v+.03*this.car.a)),0==this.car.yaw_rate)this.car.x+=this.car.v*Math.cos(this.car.rotation)*t,this.car.y+=this.car.v*Math.sin(this.car.rotation)*t;else{var e=.01*this.car.yaw_rate*(this.car.v+.001)*Math.PI;this.car.x+=this.car.v/e*(Math.sin(this.car.rotation+e*t)-Math.sin(this.car.rotation)),this.car.y+=this.car.v/e*(Math.cos(this.car.rotation)-Math.cos(this.car.rotation+e*t)),this.car.rotation+=e*t}this.car.mx=Math.floor(this.car.x/d),this.car.my=Math.floor(this.car.y/d),this.car.checkAndsetNewRoad(),this.lidar.x=this.car.x,this.lidar.y=this.car.y,this.lidar.rotation=this.car.rotation;var i=this.detectInteractions(!0,!0),o=i.agentCollisions,n=i.onRoad;return o.length>0&&(this.car.v=0,this.car.vy=0),{agentCollisions:o,onRoad:n}},e}(M),j={env:k,editor:U,level:{fullCity:"embedded://level/fullCity",level1:"embedded://level/level1",level2:"embedded://level/level2",level3:"embedded://level/level3",level0:"embedded://level/level0"},motion:{BasicMotion:R,ControlMotion:B}};e.default=j}]).default; \ No newline at end of file diff --git a/demo/webapp/ddpg-traffic.html b/demo/webapp/ddpg-traffic.html index ff6108b..f522764 100644 --- a/demo/webapp/ddpg-traffic.html +++ b/demo/webapp/ddpg-traffic.html @@ -32,7 +32,6 @@

Current state (Lidar points)




  • Deep Deterministic Policy Gradients (DDPG): paper
  • Parameter Space Noise for Exploration: paper
    • -
  • Prioritized Experience Replay: paper

You can use the arrow keys to control the car by yourself.

@@ -58,6 +57,7 @@

Current state (Lidar points)




+ diff --git a/demo/webapp/ddpg.html b/demo/webapp/ddpg.html index 1fa1e0a..da58b53 100644 --- a/demo/webapp/ddpg.html +++ b/demo/webapp/ddpg.html @@ -32,7 +32,6 @@

Current state (Lidar points)




  • Deep Deterministic Policy Gradients (DDPG): paper
  • Parameter Space Noise for Exploration: paper
    • -
  • Prioritized Experience Replay: paper

You can use the arrow keys to control the car by yourself.

@@ -57,6 +56,7 @@

Current state (Lidar points)




+ diff --git a/demo/webapp/index.html b/demo/webapp/index.html index 09c30eb..8a3f655 100644 --- a/demo/webapp/index.html +++ b/demo/webapp/index.html @@ -71,7 +71,6 @@

  • Deep Deterministic Policy Gradients (DDPG): paper
  • Parameter Space Noise for Exploration: paper
    • -
  • Prioritized Experience Replay: paper

@@ -90,7 +89,6 @@

  • Deep Deterministic Policy Gradients (DDPG): paper
  • Parameter Space Noise for Exploration: paper
    • -
  • Prioritized Experience Replay: paper

The control is based on two continuous values for the throttle and steering angle of the car. diff --git a/demo/webapp/public/js/ddpg-traffic/index.js b/demo/webapp/public/js/ddpg-traffic/index.js index 141181d..db93f4d 100644 --- a/demo/webapp/public/js/ddpg-traffic/index.js +++ b/demo/webapp/public/js/ddpg-traffic/index.js @@ -63,8 +63,7 @@ env.load().then(() => { }); env.addEvent("load", () => { - //agent.restore("ddpg-traffic", "model-ddpg-traffic") - agent.restore("four", "model-ddpg-traffic-epoch-120"); + agent.restore("ddpg-traffic", "model-ddpg-traffic-epoch-120"); }); }); diff --git a/demo/webapp/public/js/ddpg/ddpg.js b/demo/webapp/public/js/ddpg/ddpg.js index e8175c4..1f79b99 100644 --- a/demo/webapp/public/js/ddpg/ddpg.js +++ b/demo/webapp/public/js/ddpg/ddpg.js @@ -179,6 +179,7 @@ class DDPG { return erros.mean(); }, true, this.criticWeights); + // For experience Replay this.memory.appendBackWithCost(batch, costs); const loss = criticLoss.buffer().values[0]; diff --git a/demo/webapp/public/js/ddpg/ddpg_agent.js b/demo/webapp/public/js/ddpg/ddpg_agent.js index bee45ee..95f922f 100644 --- a/demo/webapp/public/js/ddpg/ddpg_agent.js +++ b/demo/webapp/public/js/ddpg/ddpg_agent.js @@ -25,7 +25,7 @@ class DDPGAgent { "nbEpochs": config.nbEpochs || 200, "nbEpochsCycle": config.nbEpochsCycle || 10, "nbTrainSteps": config.nbTrainSteps || 110, - "tau": config.tau || 0.01, + "tau": config.tau || 0.008, "initialStddev": config.initialStddev || 0.1, "desiredActionStddev": config.desiredActionStddev || 0.1, "adoptionCoefficient": config.adoptionCoefficient || 1.01, @@ -48,7 +48,7 @@ class DDPGAgent { // Buffer replay // The baseline use 1e6 but this size should be enough for this problem - this.memory = new Memory(this.config.memorySize); + this.memory = new PrioritizedMemory(this.config.memorySize); // Actor and Critic are from js/DDPG/models.js this.actor = new Actor(this.config); this.critic = new Critic(this.config); @@ -75,8 +75,8 @@ class DDPGAgent { /* Restore the weights of the network */ - const critic = await tf.loadModel('http://localhost:3000/public/models/'+folder+'/critic-'+name+'.json'); - const actor = await tf.loadModel("http://localhost:3000/public/models/"+folder+"/actor-"+name+".json"); + const critic = await tf.loadModel('https://metacar-project.com/public/models/'+folder+'/critic-'+name+'.json'); + const actor = await tf.loadModel("https://metacar-project.com/public/models/"+folder+"/actor-"+name+".json"); this.ddpg.critic = copyFromSave(critic, Critic, this.config, this.ddpg.obsInput, this.ddpg.actionInput); this.ddpg.actor = copyFromSave(actor, Actor, this.config, this.ddpg.obsInput, this.ddpg.actionInput); diff --git a/demo/webapp/public/js/ddpg/index.js b/demo/webapp/public/js/ddpg/index.js index 984feae..bbe6fd3 100644 --- a/demo/webapp/public/js/ddpg/index.js +++ b/demo/webapp/public/js/ddpg/index.js @@ -7,10 +7,7 @@ env.setAgentLidar({pts: 5, width: 3, height: 7, pos: -0.5}) // js/DDPG/ddpg.js var agent = new DDPGAgent(env, { - stateSize: 26, - resetEpisode: true, - saveDuringTraining: true, - saveInterval: 10, + stateSize: 26 }); initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); diff --git a/demo/webapp/public/js/ddpg/memory.js b/demo/webapp/public/js/ddpg/memory.js index b4921fb..fc8db0a 100644 --- a/demo/webapp/public/js/ddpg/memory.js +++ b/demo/webapp/public/js/ddpg/memory.js @@ -6,82 +6,24 @@ class Memory { */ constructor(maxlen){ this.maxlen = maxlen; - this.buffer = []; - this.priorBuffer = []; + this.length = 0; + this.start = 0; + + this.obs0List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.obs1List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.rewardsList = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.actionsList = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.terminals1List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); } /** - * Sample a batch - * @param batchSize (number) - * @return batch [] + * @param idx (number) */ - getBatch(batchSize){ - const batch = { - 'obs0': [], - 'obs1': [], - 'rewards': [], - 'actions': [], - 'terminals': [], - }; - - if (batchSize > this.priorBuffer.length){ - console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); - return batch; - } - - for (let b=0; b < batchSize/2; b++){ - let id = Math.floor(Math.random() * this.priorBuffer.length); - batch.obs0.push(this.priorBuffer[id].obs0); - batch.obs1.push(this.priorBuffer[id].obs1); - batch.rewards.push(this.priorBuffer[id].reward); - batch.actions.push(this.priorBuffer[id].action); - batch.terminals.push(this.priorBuffer[id].terminal); - } - return batch - } - - _bufferBatch(batchSize){ - const batch = { - 'obs0': [], - 'obs1': [], - 'rewards': [], - 'actions': [], - 'terminals': [], - }; - - for (let b=0; b < batchSize/2; b++){ - let nElem = this.buffer.pop(); - batch.obs0.push(nElem.obs0); - batch.obs1.push(nElem.obs1); - batch.rewards.push(nElem.reward); - batch.actions.push(nElem.action); - batch.terminals.push(nElem.terminal); - } - - for (let b=0; b < batchSize/2; b++){ - let id = Math.floor(Math.random() * this.buffer.length); - batch.obs0.push(this.buffer[id].obs0); - batch.obs1.push(this.buffer[id].obs1); - batch.rewards.push(this.buffer[id].reward); - batch.actions.push(this.buffer[id].action); - batch.terminals.push(this.buffer[id].terminal); - this.buffer.splice(id, 1); - } - - return batch - } - - _addRandomBufferBatch(batchSize, batch){ - for (let b=0; b < batchSize; b++){ - let id = Math.floor(Math.random() * this.buffer.length); - batch.obs0.push(this.buffer[id].obs0); - batch.obs1.push(this.buffer[id].obs1); - batch.rewards.push(this.buffer[id].reward); - batch.actions.push(this.buffer[id].action); - batch.terminals.push(this.buffer[id].terminal); - this.buffer.splice(id, 1); + getItem(idx){ + if (idx < 0 || idx >= this.length){ + console.error("Memory.getItem: idx not in range."); } - return batch + return this.data[(this.start + idx) % this.maxlen] } /** @@ -89,19 +31,8 @@ class Memory { * @param batchSize (number) * @return batch [] */ - popBatch(batchSize){ - let originalBatchSize = batchSize; - let priorBufferBatchSize; - let bufferBatchSize; - if (batchSize % 2 != 0){ - console.warn("Batch size should be a even.") - } - if (this.priorBuffer.length < batchSize/2){ - //console.log("get full batch from buffer"); - const batch = this._bufferBatch(batchSize); - console.assert(batch.obs0.length == batchSize); - return batch; - } + getBatch(batchSize){ + const arrLength = this.length; const batch = { 'obs0': [], 'obs1': [], @@ -110,80 +41,19 @@ class Memory { 'terminals': [], }; if (batchSize > this.length){ - console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); return batch; } - - if (this.buffer.length > 0){ - //console.log("Get half of prior and other from buffer."); - batchSize = batchSize / 2; - } - else{ - //console.log("Get all from priorBuffer"); - } - for (let b=0; b < batchSize; b++){ - let id = Math.floor(Math.random() * this.priorBuffer.length); - batch.obs0.push(this.priorBuffer[id].obs0); - batch.obs1.push(this.priorBuffer[id].obs1); - batch.rewards.push(this.priorBuffer[id].reward); - batch.actions.push(this.priorBuffer[id].action); - batch.terminals.push(this.priorBuffer[id].terminal); - this.priorBuffer.splice(id, 1); + let id = Math.floor(Math.random() * arrLength); + batch.obs0.push(this.obs0List[id]); + batch.obs1.push(this.obs1List[id]); + batch.rewards.push(this.rewardsList[id]); + batch.actions.push(this.actionsList[id]); + batch.terminals.push(this.terminals1List[id]); } - - if (this.buffer.length > 0){ - this._addRandomBufferBatch(batchSize, batch); - } - console.assert(batch.obs0.length == originalBatchSize); return batch } - _insert(element, array) { - if (array.length == 0 || element.cost < array[0].cost || array[0].cost == null){ - array.unshift(element); - return array; - } - array.splice(this._locationOf(element, array) + 1, 0, element); - return array; - } - - _locationOf(element, array, start, end) { - start = start || 0; - end = end || array.length; - - var pivot = parseInt(start + (end - start) / 2, 10); - - if (end-start <= 1 || array[pivot] === element) return pivot; - - if (array[pivot].cost != null && array[pivot].cost < element.cost) { - return this._locationOf(element, array, pivot, end); - } else { - return this._locationOf(element, array, start, pivot); - } - } - - /** - * @param batch (Object) from getBatch() - * @param cost (number) Cost associated with each row of the batch - */ - appendBackWithCost(batch, costs){ - for (let b=0; b < batch.obs0.length; b++){ - if (this.buffer.length == this.maxlen){ - this.buffer.shift(); - } - this._insert({ - obs0: batch.obs0[b], - action: batch.actions[b], - reward: batch.rewards[b], - obs1: batch.obs1[b], - terminal: batch.terminals[b], - cost: costs[b] - }, this.buffer); - } - console.assert(this.buffer.length <= this.maxlen); - } - /** * @param obs0 [] * @param action (number) @@ -191,50 +61,23 @@ class Memory { * @param obs1 [] * @param terminal1 (boolean) */ - append(obs0, action, reward, obs1, terminal){ - if (this.priorBuffer.length == this.maxlen){ - this.priorBuffer.shift(); - } - this.priorBuffer.push({ - obs0: obs0, - action: action, - reward: reward, - obs1: obs1, - terminal: terminal, - cost: null - }); - console.assert(this.priorBuffer.length <= this.maxlen); + append(obs0, action, reward, obs1, terminal1){ + if (this.length < this.maxlen){ + this.length += 1; + } + else if (this.length == this.maxlen) { + //this.obs0List[(this.start + this.length - 1) % this.maxlen].dispose(); + //this.obs1List[(this.start + this.length - 1) % this.maxlen].dispose(); + //this.actionsList[(this.start + this.length - 1) % this.maxlen].dispose(); + this.start = (this.start + 1) % this.maxlen; + } + else { + console.error("Memory.append: This should never be printed"); + } + this.obs0List[(this.start + this.length - 1) % this.maxlen] = obs0; + this.obs1List[(this.start + this.length - 1) % this.maxlen] = obs1; + this.rewardsList[(this.start + this.length - 1) % this.maxlen] = reward; + this.actionsList[(this.start + this.length - 1) % this.maxlen] = action; + this.terminals1List[(this.start + this.length - 1) % this.maxlen] = terminal1; } -} -/* -var mem = new Memory(20000); -Math.seedrandom(0); -console.assert(mem.length == 0); - -var array = []; -for (let i=1; i < 40000; i++){ - mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); -} - -console.assert(mem.length == 20000); -console.assert(mem.list[0].obs0 == "obs0-20000"); -console.assert(mem.list[19999].obs0 == "obs0-39999"); - -let batch = mem.getBatch(32); - -console.assert(batch.obs0.length == 32); -console.assert(mem.length == 20000 - 32); - -let costs = []; -for (i=31; i >= 0; i--){ - costs.push(i); -} -mem.appendBackWithCost(batch, costs); - -console.log(mem.list); - -/* -for (let i=1; i < 64; i++){ - mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); -} -*/ \ No newline at end of file +} \ No newline at end of file diff --git a/demo/webapp/public/js/ddpg/prioritized_memory.js b/demo/webapp/public/js/ddpg/prioritized_memory.js new file mode 100644 index 0000000..5a7f74b --- /dev/null +++ b/demo/webapp/public/js/ddpg/prioritized_memory.js @@ -0,0 +1,240 @@ + +class PrioritizedMemory { + + /** + * @param maxlen (number) Buffer limit + */ + constructor(maxlen){ + this.maxlen = maxlen; + this.buffer = []; + this.priorBuffer = []; + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + getBatch(batchSize){ + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + + if (batchSize > this.priorBuffer.length){ + console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); + return batch; + } + + for (let b=0; b < batchSize/2; b++){ + let id = Math.floor(Math.random() * this.priorBuffer.length); + batch.obs0.push(this.priorBuffer[id].obs0); + batch.obs1.push(this.priorBuffer[id].obs1); + batch.rewards.push(this.priorBuffer[id].reward); + batch.actions.push(this.priorBuffer[id].action); + batch.terminals.push(this.priorBuffer[id].terminal); + } + return batch + } + + _bufferBatch(batchSize){ + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + + for (let b=0; b < batchSize/2; b++){ + let nElem = this.buffer.pop(); + batch.obs0.push(nElem.obs0); + batch.obs1.push(nElem.obs1); + batch.rewards.push(nElem.reward); + batch.actions.push(nElem.action); + batch.terminals.push(nElem.terminal); + } + + for (let b=0; b < batchSize/2; b++){ + let id = Math.floor(Math.random() * this.buffer.length); + batch.obs0.push(this.buffer[id].obs0); + batch.obs1.push(this.buffer[id].obs1); + batch.rewards.push(this.buffer[id].reward); + batch.actions.push(this.buffer[id].action); + batch.terminals.push(this.buffer[id].terminal); + this.buffer.splice(id, 1); + } + + return batch + } + + _addRandomBufferBatch(batchSize, batch){ + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * this.buffer.length); + batch.obs0.push(this.buffer[id].obs0); + batch.obs1.push(this.buffer[id].obs1); + batch.rewards.push(this.buffer[id].reward); + batch.actions.push(this.buffer[id].action); + batch.terminals.push(this.buffer[id].terminal); + this.buffer.splice(id, 1); + } + return batch + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + popBatch(batchSize){ + let originalBatchSize = batchSize; + let priorBufferBatchSize; + let bufferBatchSize; + if (batchSize % 2 != 0){ + console.warn("Batch size should be a even.") + } + if (this.priorBuffer.length < batchSize/2){ + //console.log("get full batch from buffer"); + const batch = this._bufferBatch(batchSize); + console.assert(batch.obs0.length == batchSize); + return batch; + } + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + if (batchSize > this.length){ + console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); + return batch; + } + + if (this.buffer.length > 0){ + //console.log("Get half of prior and other from buffer."); + batchSize = batchSize / 2; + } + else{ + //console.log("Get all from priorBuffer"); + } + + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * this.priorBuffer.length); + batch.obs0.push(this.priorBuffer[id].obs0); + batch.obs1.push(this.priorBuffer[id].obs1); + batch.rewards.push(this.priorBuffer[id].reward); + batch.actions.push(this.priorBuffer[id].action); + batch.terminals.push(this.priorBuffer[id].terminal); + this.priorBuffer.splice(id, 1); + } + + if (this.buffer.length > 0){ + this._addRandomBufferBatch(batchSize, batch); + } + console.assert(batch.obs0.length == originalBatchSize); + return batch + } + + _insert(element, array) { + if (array.length == 0 || element.cost < array[0].cost || array[0].cost == null){ + array.unshift(element); + return array; + } + array.splice(this._locationOf(element, array) + 1, 0, element); + return array; + } + + _locationOf(element, array, start, end) { + start = start || 0; + end = end || array.length; + + var pivot = parseInt(start + (end - start) / 2, 10); + + if (end-start <= 1 || array[pivot] === element) return pivot; + + if (array[pivot].cost != null && array[pivot].cost < element.cost) { + return this._locationOf(element, array, pivot, end); + } else { + return this._locationOf(element, array, start, pivot); + } + } + + /** + * @param batch (Object) from getBatch() + * @param cost (number) Cost associated with each row of the batch + */ + appendBackWithCost(batch, costs){ + for (let b=0; b < batch.obs0.length; b++){ + if (this.buffer.length == this.maxlen){ + this.buffer.shift(); + } + this._insert({ + obs0: batch.obs0[b], + action: batch.actions[b], + reward: batch.rewards[b], + obs1: batch.obs1[b], + terminal: batch.terminals[b], + cost: costs[b] + }, this.buffer); + } + console.assert(this.buffer.length <= this.maxlen); + } + + /** + * @param obs0 [] + * @param action (number) + * @param reward (number) + * @param obs1 [] + * @param terminal1 (boolean) + */ + append(obs0, action, reward, obs1, terminal){ + if (this.priorBuffer.length == this.maxlen){ + this.priorBuffer.shift(); + } + this.priorBuffer.push({ + obs0: obs0, + action: action, + reward: reward, + obs1: obs1, + terminal: terminal, + cost: null + }); + console.assert(this.priorBuffer.length <= this.maxlen); + } +} +/* +var mem = new Memory(20000); +Math.seedrandom(0); +console.assert(mem.length == 0); + +var array = []; +for (let i=1; i < 40000; i++){ + mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); +} + +console.assert(mem.length == 20000); +console.assert(mem.list[0].obs0 == "obs0-20000"); +console.assert(mem.list[19999].obs0 == "obs0-39999"); + +let batch = mem.getBatch(32); + +console.assert(batch.obs0.length == 32); +console.assert(mem.length == 20000 - 32); + +let costs = []; +for (i=31; i >= 0; i--){ + costs.push(i); +} +mem.appendBackWithCost(batch, costs); + +console.log(mem.list); + +/* +for (let i=1; i < 64; i++){ + mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); +} +*/ \ No newline at end of file diff --git a/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.json b/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.json new file mode 100644 index 0000000..728c09d --- /dev/null +++ b/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.json @@ -0,0 +1 @@ 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e=0,i=0;ithis.state[o][n]||t[a].mapId==u.ROAD&&this.state[o][n]==u.DEFAULT)&&(this.state[o][n]=t[a].mapId)}e+=1}},t}(),b=(I=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])},function(t,e){function i(){this.constructor=t}I(t,e),t.prototype=null===e?Object.create(e):(i.prototype=e.prototype,new i)}),R=function(t){function e(e,i){var o=t.call(this,e)||this;return o.rotationStep=i.rotationStep||.5,o.actions=i.actions||["UP","LEFT","RIGHT","DOWN","WAIT"],o}return b(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i0&&(this.car.v=0,this.car.vy=0),{agentCollisions:i,onRoad:o}},e}(M),E=function(){var t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])};return function(e,i){function o(){this.constructor=e}t(e,i),e.prototype=null===i?Object.create(i):(o.prototype=i.prototype,new o)}}(),L=function(t){function e(e){var i=t.call(this,e)||this;return i.map=i.level.getMap(),i.mapSizeY=i.map.length,i.mapSizeX=i.map[0].length,i.rotationStep=.5,i.rotationToNextCase={},i.rotationToNextCase[0]={mx:1,my:0},i.rotationToNextCase[.5]={mx:0,my:1},i.rotationToNextCase[1]={mx:-1,my:0},i.rotationToNextCase[1.5]={mx:0,my:-1},i.rotationToNextCase[-.5]={mx:0,my:-1},i.rotationToNextCase[-1]={mx:-1,my:0},i.rotationToNextCase[-1.5]={mx:0,my:1},i}return E(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i=this.mapSizeY||t<0||t>=this.mapSizeX||0==this.map[e][t])return!1;if(i&&this.level.getRoad(e,t).cars.length>0)return!1;return!0},e.prototype.autoRotation=function(){var t=d+d/3,e=d-d/3.5,i=d;if(this.car.next_road){var o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var a=this.car.rotation/Math.PI,r=(this.car.rotation/Math.PI+.5)%2,s=(this.car.rotation/Math.PI-.5)%2,h=this.rotationToNextCase[a],c=this.rotationToNextCase[r],l=this.rotationToNextCase[s],p=!1,u=this.car.mx+h.mx,v=this.car.my+h.my;if(this.isRoad(u,v)?this.car.haveTurned||this.car.turnedRandom?!this.car.haveTurned&&this.car.turnedRandom<.5&&this.isRoad(this.car.mx+c.mx,this.car.my+c.my)?(p=!0,this.car.optionalTurn=!0):!this.car.haveTurned&&this.car.turnedRandom>=.5&&this.isRoad(this.car.mx+l.mx,this.car.my+l.my)&&(p=!0,this.car.optionalTurn=!0):this.car.turnedRandom=Math.random():(p=!0,this.car.optionalTurn=!1),p)for(var y=[(a+.5)%2,(a-.5)%2,null],m=0;m=1&&n&&n.core.line==i&&(i=0==i?1:0),e.line=i,e.x=t.x,e.y=t.y,e.x+=d/2,e.y+=d/2;var a=1*d/4;t.orientation=Math.floor(t.orientation/e.rotationStep)*e.rotationStep;var r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e){void 0===e&&(e=null);for(var i=0;i0&&0==this.car.a?this.car.v=Math.max(0,this.car.v-.01):this.car.v<0&&0==this.car.a&&(this.car.v=Math.min(0,this.car.v+.01)),this.car.a>0&&this.car.v>=0?this.car.v=Math.min(1.5,this.car.v+.01*this.car.a):this.car.a>0&&this.car.v<0&&(this.car.v=Math.min(1.5,this.car.v+.03*this.car.a)),this.car.a<0&&this.car.v<=0?this.car.v=Math.max(-1.5,this.car.v+.01*this.car.a):this.car.a<0&&this.car.v>0&&(this.car.v=Math.max(-1.5,this.car.v+.03*this.car.a)),0==this.car.yaw_rate)this.car.x+=this.car.v*Math.cos(this.car.rotation)*t,this.car.y+=this.car.v*Math.sin(this.car.rotation)*t;else{var 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0,this.core.carId=C,C+=1,this.core.agent=!1,this.core.obstacle=!0,this.core.mapId=y.CAR,o.lidar&&(this.createLidar(o.lidarInfo),this.core.lidar=this.lidar),o.motionEngine?(this.motion=o.motionEngine,this.motion.setUp(this.core,this.lidar),this.core.rotationStep=.5):this.core.rotationStep=.5,this.core.mx=this.info.mx,this.core.my=this.info.my,this.core.x=this.info.mx*d,this.core.y=this.info.my*d,this.core.checkAndsetNewRoad=function(t){return n.checkAndsetNewRoad(t)};var r=this.level.getRoad(this.core.my,this.core.mx);this.core.line=0,r&&r.setCarPosition(this.core,this.info.line)}return t.prototype.checkAndsetNewRoad=function(t){var e=this.core.road;t||(t=this.level.getRoad(this.core.my,this.core.mx)),t!=e&&(e&&e.cars.splice(e.cars.indexOf(this.core.carId),1),this.core.road=t,this.core.haveTurned=!1,this.core.optionalTurn=!1,this.core.turnedRandom=void 0,this.core.next_road=!1,t&&t.cars.push(this.core.carId))},t.prototype.reset=function(){this.core.mx=this.info.mx,this.core.my=this.info.my;var t=this.level.getRoad(this.core.my,this.core.mx);t&&t.setCarPosition(this.core,this.info.line,!0)},t.prototype.getState=function(){var t={},e=[];return this.motion.state.map(function(t){e=e.concat(t)}),e.push(this.core.v),t.linear=e,t.lidar=this.motion.state.map(function(t){return t.slice()}),t.v=this.core.v,t.a=this.core.last_a,t.steering=this.core.last_yaw_rate,t},t.prototype.step=function(t,e){if(void 0===e&&(e=null),null==e)var i=this.motion.step(t),o=i.agentCollisions,n=i.onRoad;else{var a=this.motion.actionStep(t,e);o=a.agentCollisions,n=a.onRoad}return{agentCollisions:o,onRoad:n}},t.prototype.createLidar=function(t){t.pts=t.pts||5,t.width=t.width||4,t.height=t.height||5,t.pos=t.pos||0,this.lidar=new s,this.lidar.lidarPts=[],this.lidar.collisionPts=[];var e=new r;e.pt=!1,e.alpha=.3,e.beginFill(5329233),e.drawRect(0,0,this.core.width*t.width,this.core.height*t.height),e.endFill();var i=new r;i.pt=!1,i.alpha=0,i.beginFill(5308416),i.drawRect(this.core.width-5,e.height/2,3,3),i.endFill();var o=new r;o.pt=!1,o.alpha=0,o.beginFill(5308416),o.drawRect(-1,e.height/2-2,3,3),o.endFill();var n=new r;n.pt=!1,n.alpha=0,n.beginFill(5308416),n.drawRect(this.core.width/2,e.height/2-this.core.height/2+5,3,3),n.endFill();var a=new r;a.pt=!1,a.alpha=0,a.beginFill(5308416),a.drawRect(this.core.width/2,e.height/2+this.core.height/2-8,3,3),a.endFill(),o.x-=t.pos*this.core.width,i.x-=t.pos*this.core.width,a.x-=t.pos*this.core.width,n.x-=t.pos*this.core.width;for(var h=e.width/t.pts,c=e.height/t.pts,l=t.pts-1;l>=0;l--)for(var d=0;do.x&&i.xo.y&&i.y=4||!t)&&(r=!0),{agentCollisions:o,onRoad:r}},t.prototype.setState=function(t){for(var e=0,i=0;ithis.state[o][n]||t[a].mapId==y.ROAD&&this.state[o][n]==y.DEFAULT)&&(this.state[o][n]=t[a].mapId)}e+=1}},t}(),b=(I=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])},function(t,e){function i(){this.constructor=t}I(t,e),t.prototype=null===e?Object.create(e):(i.prototype=e.prototype,new i)}),R=function(t){function e(e,i){var o=t.call(this,e)||this;return o.maxSpeed=1,o.rotationStep=i.rotationStep||.5,o.actions=i.actions||["UP","LEFT","RIGHT","DOWN","WAIT"],o}return b(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i0&&(this.car.v=0,this.car.vy=0),{agentCollisions:i,onRoad:o}},e}(M),E=function(){var t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])};return function(e,i){function o(){this.constructor=e}t(e,i),e.prototype=null===i?Object.create(i):(o.prototype=i.prototype,new o)}}(),S=function(t){function e(e){var i=t.call(this,e)||this;return i.map=i.level.getMap(),i.mapSizeY=i.map.length,i.mapSizeX=i.map[0].length,i.rotationStep=.5,i.rotationToNextCase={},i.rotationToNextCase[0]={mx:1,my:0},i.rotationToNextCase[.5]={mx:0,my:1},i.rotationToNextCase[1]={mx:-1,my:0},i.rotationToNextCase[1.5]={mx:0,my:-1},i.rotationToNextCase[-.5]={mx:0,my:-1},i.rotationToNextCase[-1]={mx:-1,my:0},i.rotationToNextCase[-1.5]={mx:0,my:1},i}return E(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i=this.mapSizeY||t<0||t>=this.mapSizeX||0==this.map[e][t])return!1;if(i&&this.level.getRoad(e,t).cars.length>0)return!1;return!0},e.prototype.autoRotation=function(){var t=d+d/3,e=d-d/3.5,i=d;if(this.car.next_road){var o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var a=this.car.rotation/Math.PI,r=(this.car.rotation/Math.PI+.5)%2,s=(this.car.rotation/Math.PI-.5)%2,h=this.rotationToNextCase[a],c=this.rotationToNextCase[r],l=this.rotationToNextCase[s],p=!1,y=this.car.mx+h.mx,u=this.car.my+h.my;if(this.isRoad(y,u)?this.car.haveTurned||this.car.turnedRandom?!this.car.haveTurned&&this.car.turnedRandom<.5&&this.isRoad(this.car.mx+c.mx,this.car.my+c.my)?(p=!0,this.car.optionalTurn=!0):!this.car.haveTurned&&this.car.turnedRandom>=.5&&this.isRoad(this.car.mx+l.mx,this.car.my+l.my)&&(p=!0,this.car.optionalTurn=!0):this.car.turnedRandom=Math.random():(p=!0,this.car.optionalTurn=!1),p)for(var x=[(a+.5)%2,(a-.5)%2,null],v=0;v=1&&n&&n.core.line==i&&(i=0==i?1:0),e.line=i,e.a=0,e.v=0,e.x=t.x,e.y=t.y,e.x+=d/2,e.y+=d/2;var a=1*d/4;t.orientation=Math.floor(t.orientation/e.rotationStep)*e.rotationStep;var r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e,i){if(void 0===e&&(e=null),void 0===i&&(i=!0),!i||this.steping){for(var o=0;o0&&0==this.car.a?this.car.v=Math.max(0,this.car.v-.01):this.car.v<0&&0==this.car.a&&(this.car.v=Math.min(0,this.car.v+.01)),this.car.a>0&&this.car.v>=0?this.car.v=Math.min(this.maxSpeed,this.car.v+.005*this.car.a):this.car.a>0&&this.car.v<0&&(this.car.v=Math.min(this.maxSpeed,this.car.v+.03*this.car.a)),this.car.a<0&&this.car.v<=0?this.car.v=Math.max(-this.maxSpeed,this.car.v+.005*this.car.a):this.car.a<0&&this.car.v>0&&(this.car.v=Math.max(-this.maxSpeed,this.car.v+.03*this.car.a)),0==this.car.yaw_rate)this.car.x+=this.car.v*Math.cos(this.car.rotation)*t,this.car.y+=this.car.v*Math.sin(this.car.rotation)*t;else{var e=.01*this.car.yaw_rate*(this.car.v+.001)*Math.PI;this.car.x+=this.car.v/e*(Math.sin(this.car.rotation+e*t)-Math.sin(this.car.rotation)),this.car.y+=this.car.v/e*(Math.cos(this.car.rotation)-Math.cos(this.car.rotation+e*t)),this.car.rotation+=e*t}this.car.mx=Math.floor(this.car.x/d),this.car.my=Math.floor(this.car.y/d),this.car.checkAndsetNewRoad(),this.lidar.x=this.car.x,this.lidar.y=this.car.y,this.lidar.rotation=this.car.rotation;var i=this.detectInteractions(!0,!0),o=i.agentCollisions,n=i.onRoad;return o.length>0&&(this.car.v=0,this.car.vy=0),{agentCollisions:o,onRoad:n}},e}(M),j={env:k,editor:U,level:{fullCity:"embedded://level/fullCity",level1:"embedded://level/level1",level2:"embedded://level/level2",level3:"embedded://level/level3",level0:"embedded://level/level0"},motion:{BasicMotion:R,ControlMotion:B}};e.default=j}]).default; \ No newline at end of file