From ef6292b1f194727c1f799f9a317c220330ad9a97 Mon Sep 17 00:00:00 2001 From: Federico-PizarroBejarano Date: Fri, 5 Jan 2024 15:57:47 -0500 Subject: [PATCH] Adding basic training of PPO on crazyflie with MPSF in the loop --- .../config_overrides/crazyflie_track.yaml | 50 ++++++ .../config_overrides/ppo_crazyflie.yaml | 47 ++++++ experiments/crazyflie/crazyflie_experiment.py | 40 +++-- .../models/rl_models/ppo/model_best.pt | Bin 0 -> 470123 bytes experiments/crazyflie/plotting_results.py | 2 - experiments/crazyflie/test_crazyflie.sh | 11 +- experiments/crazyflie/train_rl.py | 113 ++++++++++++++ .../controllers/firmware/firmware_wrapper.py | 2 +- safe_control_gym/controllers/ppo/ppo.py | 143 +++++++++--------- .../envs/gym_pybullet_drones/quadrotor.py | 39 +---- safe_control_gym/safety_filters/mpsc/mpsc.py | 3 + 11 files changed, 324 insertions(+), 126 deletions(-) create mode 100644 experiments/crazyflie/config_overrides/ppo_crazyflie.yaml create mode 100644 experiments/crazyflie/models/rl_models/ppo/model_best.pt create mode 100644 experiments/crazyflie/train_rl.py diff --git a/experiments/crazyflie/config_overrides/crazyflie_track.yaml b/experiments/crazyflie/config_overrides/crazyflie_track.yaml index 27b040bda..60cd6bf42 100644 --- a/experiments/crazyflie/config_overrides/crazyflie_track.yaml +++ b/experiments/crazyflie/config_overrides/crazyflie_track.yaml @@ -26,6 +26,56 @@ task_config: randomized_init: False randomized_inertial_prop: False + init_state_randomization_info: + init_x: + distrib: 'uniform' + low: -0.75 + high: 0.75 + init_x_dot: + distrib: 'uniform' + low: -0.5 + high: 0.5 + init_y: + distrib: 'uniform' + low: -0.75 + high: 0.75 + init_y_dot: + distrib: 'uniform' + low: -0.5 + high: 0.5 + init_z: + distrib: 'uniform' + low: 0.5 + high: 2 + init_z_dot: + distrib: 'uniform' + low: -1 + high: 1 + init_phi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_theta: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_psi: + distrib: 'uniform' + low: -0.2 + high: 0.2 + init_p: + distrib: 'uniform' + low: -1 + high: 1 + init_q: + distrib: 'uniform' + low: -1 + high: 1 + init_r: + distrib: 'uniform' + low: -1 + high: 1 + task: traj_tracking task_info: trajectory_type: figure8 diff --git a/experiments/crazyflie/config_overrides/ppo_crazyflie.yaml b/experiments/crazyflie/config_overrides/ppo_crazyflie.yaml new file mode 100644 index 000000000..61ecd4bcd --- /dev/null +++ b/experiments/crazyflie/config_overrides/ppo_crazyflie.yaml @@ -0,0 +1,47 @@ +algo: ppo +algo_config: + # model args + hidden_dim: 128 + activation: 'leaky_relu' + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # loss args + gamma: 0.98 + use_gae: False + gae_lambda: 0.8 + use_clipped_value: False + clip_param: 0.1 + target_kl: 1.587713889686473e-07 + entropy_coef: 0.00010753631441212628 + + # optim args + opt_epochs: 5 + mini_batch_size: 128 + actor_lr: 0.0007948148615930024 + critic_lr: 0.007497368468753617 + max_grad_norm: 0.5 + + # runner args + max_env_steps: 100000 + num_workers: 1 + rollout_batch_size: 1 + rollout_steps: 500 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 6000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 6000 + eval_save_best: True + tensorboard: False + + # safety filter + filter_train_actions: True + penalize_sf_diff: True + sf_penalty: 1 + use_safe_reset: True diff --git a/experiments/crazyflie/crazyflie_experiment.py b/experiments/crazyflie/crazyflie_experiment.py index cb6f6ac62..2bd5eb16f 100644 --- a/experiments/crazyflie/crazyflie_experiment.py +++ b/experiments/crazyflie/crazyflie_experiment.py @@ -1,6 +1,7 @@ '''Running MPSC using the crazyflie firmware. ''' import sys +import shutil import time sys.path.insert(0, '/home/federico/GitHub/safe-control-gym') @@ -54,8 +55,11 @@ def run(gui=False, plot=True, training=False, certify=True, curr_path='.'): **config.task_config) FIRMWARE_FREQ = 500 + config.task_config.gui = gui config.task_config['ctrl_freq'] = FIRMWARE_FREQ + config.algo_config['training'] = False + env_func_500 = partial(make, config.task, **config.task_config) @@ -80,11 +84,20 @@ def run(gui=False, plot=True, training=False, certify=True, curr_path='.'): ctrl = make(config.algo, env_func, **config.algo_config) - ctrl.gain = lqr_gain - ctrl.model.U_EQ = np.array([[0, 0]]).T - ctrl.env.X_GOAL = full_trajectory - ctrl.env.TASK = Task.TRAJ_TRACKING + if config.algo in ['ppo', 'sac', 'safe_explorer_ppo', 'cpo']: + # Load state_dict from trained. + ctrl.load(f'{curr_path}/models/rl_models/{config.algo}/model_best.pt') + + # Remove temporary files and directories + shutil.rmtree(f'{curr_path}/temp', ignore_errors=True) + ctrl.X_GOAL = full_trajectory + else: + ctrl.gain = lqr_gain + ctrl.model.U_EQ = np.array([[0, 0]]).T + + ctrl.env.X_GOAL = full_trajectory + ctrl.env.TASK = Task.TRAJ_TRACKING if certify is True: # Setup MPSC. @@ -114,8 +127,8 @@ def run(gui=False, plot=True, training=False, certify=True, curr_path='.'): curr_obs = np.atleast_2d(obs[0:4]).T curr_obs = curr_obs.reshape((4, 1)) info['current_step'] = i - new_act = ctrl.select_action(curr_obs, info) - new_act = np.clip(new_act, np.array([[-0.25, -0.25]]).T, np.array([[0.25, 0.25]]).T) + new_act = np.squeeze(ctrl.select_action(curr_obs, info)) + new_act = np.clip(new_act, np.array([-0.25, -0.25]), np.array([0.25, 0.25])) actions_uncert.append(new_act) if certify is True: certified_action, success = safety_filter.certify_action(curr_obs, new_act, info) @@ -147,15 +160,15 @@ def run(gui=False, plot=True, training=False, certify=True, curr_path='.'): states = np.array(states) actions_uncert = np.array(actions_uncert) - print('Number of Max Inputs: ', np.sum(np.abs(actions_uncert) == 0.25)) + print(f'Number of Max Inputs: {np.sum(np.abs(actions_uncert) == 0.25)}/{2*len(actions_uncert)}') actions_cert = np.array(actions_cert) corrections = np.squeeze(actions_cert) - np.squeeze(actions_uncert) # Close the environment env.close() print('Elapsed Time: ', time.time() - ep_start) - print('NUM ERRORS POS: ', np.sum(np.abs(states[:, 0]) >= 0.75)) - print('NUM ERRORS VEL: ', np.sum(np.abs(states[:, 1]) >= 0.5)) + print('NUM VIOLATIONS POS: ', np.sum(np.abs(states[:, 0]) >= 0.75)) + print('NUM VIOLATIONS VEL: ', np.sum(np.abs(states[:, 1]) >= 0.5)) print('Rate of change (inputs): ', np.linalg.norm(get_discrete_derivative(np.atleast_2d(actions_cert).T, CTRL_FREQ))) if certify: print(f'Feasible steps: {float(successes)}/{CTRL_FREQ*env.EPISODE_LEN_SEC}') @@ -169,7 +182,14 @@ def run(gui=False, plot=True, training=False, certify=True, curr_path='.'): plt.legend() plt.show() - plt.plot(states[:, 0], label='traj') + plt.plot(states[:, 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a/experiments/crazyflie/test_crazyflie.sh b/experiments/crazyflie/test_crazyflie.sh index 9e1d4b76b..6772647c7 100755 --- a/experiments/crazyflie/test_crazyflie.sh +++ b/experiments/crazyflie/test_crazyflie.sh @@ -5,23 +5,26 @@ TASK='track' MPSC='nl_mpsc' # MPSC='linear_mpsc' -# MPSC_COST='one_step_cost' +MPSC_COST='one_step_cost' # MPSC_COST='constant_cost' # MPSC_COST='regularized_cost' # MPSC_COST='lqr_cost' -MPSC_COST='precomputed_cost' +# MPSC_COST='precomputed_cost' # MPSC_COST='learned_cost' MPSC_COST_HORIZON=10 +# python3 ./train_rl.py \ python3 ./crazyflie_experiment.py \ --task quadrotor \ - --algo lqr \ + --algo ppo \ --safety_filter ${MPSC} \ --overrides \ ./config_overrides/crazyflie_${TASK}.yaml \ - ./config_overrides/lqr_crazyflie.yaml \ + ./config_overrides/ppo_crazyflie.yaml \ ./config_overrides/nl_mpsc.yaml \ + --output_dir ./models/rl_models/ppo/ \ --kv_overrides \ sf_config.cost_function=${MPSC_COST} \ sf_config.mpsc_cost_horizon=${MPSC_COST_HORIZON} \ + # task_config.init_state=None diff --git a/experiments/crazyflie/train_rl.py b/experiments/crazyflie/train_rl.py new file mode 100644 index 000000000..f7e418a57 --- /dev/null +++ b/experiments/crazyflie/train_rl.py @@ -0,0 +1,113 @@ +'''Running MPSC using the crazyflie firmware. ''' + +import os +import shutil +import sys +sys.path.insert(0, '/home/federico/GitHub/safe-control-gym') + +from functools import partial + +import numpy as np + +from experiments.crazyflie.crazyflie_utils import gen_traj +from safe_control_gym.utils.configuration import ConfigFactory +from safe_control_gym.utils.plotting import plot_from_logs +from safe_control_gym.utils.registration import make +from safe_control_gym.utils.utils import mkdirs, set_device_from_config, set_seed_from_config + +try: + import pycffirmware +except ImportError: + FIRMWARE_INSTALLED = False +else: + FIRMWARE_INSTALLED = True +finally: + print('Module \'cffirmware\' available:', FIRMWARE_INSTALLED) + + +def train(): + '''The main function creating, running, and closing an environment over N episodes. ''' + + # Define arguments. + fac = ConfigFactory() + config = fac.merge() + config.algo_config['training'] = True + + shutil.rmtree(config.output_dir, ignore_errors=True) + + set_seed_from_config(config) + set_device_from_config(config) + CTRL_FREQ = config.task_config['ctrl_freq'] + + env_func = partial(make, + config.task, + output_dir=config.output_dir, + **config.task_config) + + FIRMWARE_FREQ = 500 + config.task_config['ctrl_freq'] = FIRMWARE_FREQ + env_func_500 = partial(make, + config.task, + output_dir=config.output_dir, + **config.task_config) + + # Create environment. + firmware_wrapper = make('firmware', env_func_500, FIRMWARE_FREQ, CTRL_FREQ) + _, _ = firmware_wrapper.reset() + env = firmware_wrapper.env + + # Create trajectory. + full_trajectory = gen_traj(CTRL_FREQ, env.EPISODE_LEN_SEC) + full_trajectory = np.hstack((full_trajectory, full_trajectory)) + + # Setup controller. + ctrl = make(config.algo, + env_func, + checkpoint_path=os.path.join(config.output_dir, 'model_latest.pt'), + output_dir=config.output_dir, + seed=1, + **config.algo_config) + ctrl.reset() + + ctrl.firmware_wrapper = firmware_wrapper + ctrl.X_GOAL = full_trajectory + ctrl.CTRL_DT = 1.0 / CTRL_FREQ + + # Setup MPSC. + if config.algo in ['ppo', 'sac']: + safety_filter = make(config.safety_filter, + env_func, + **config.sf_config) + safety_filter.reset() + safety_filter.load(path=f'./models/mpsc_parameters/{config.safety_filter}_crazyflie_track.pkl') + + safety_filter.env.X_GOAL = full_trajectory + ctrl.safety_filter = safety_filter + + ctrl.learn() + ctrl.close() + print('Training done.') + + # with open(os.path.join(config.output_dir, 'config.yaml'), 'w', encoding='UTF-8') as file: + # yaml.dump(munch.unmunchify(config), file, default_flow_style=False) + + # make_plots(config) + + +def make_plots(config): + '''Produces plots for logged stats during training. + Usage + * use with `--func plot` and `--restore {dir_path}` where `dir_path` is + the experiment folder containing the logs. + * save figures under `dir_path/plots/`. + ''' + # Define source and target log locations. + log_dir = os.path.join(config.output_dir, 'logs') + plot_dir = os.path.join(config.output_dir, 'plots') + mkdirs(plot_dir) + plot_from_logs(log_dir, plot_dir, window=3) + print('Plotting done.') + + +if __name__ == '__main__': + train() diff --git a/safe_control_gym/controllers/firmware/firmware_wrapper.py b/safe_control_gym/controllers/firmware/firmware_wrapper.py index c6f6cac87..13cdf0c84 100644 --- a/safe_control_gym/controllers/firmware/firmware_wrapper.py +++ b/safe_control_gym/controllers/firmware/firmware_wrapper.py @@ -164,7 +164,7 @@ def reset(self): # Initialize controller if self.CONTROLLER == 'pid': firm.controllerPidInit() - print('PID controller init test:', firm.controllerPidTest()) + # print('PID controller init test:', firm.controllerPidTest()) elif self.CONTROLLER == 'mellinger': firm.controllerMellingerInit() assert (self.firmware_freq == 500), 'Mellinger controller requires a firmware frequency of 500Hz.' diff --git a/safe_control_gym/controllers/ppo/ppo.py b/safe_control_gym/controllers/ppo/ppo.py index 5c474fd5c..f536de4d5 100644 --- a/safe_control_gym/controllers/ppo/ppo.py +++ b/safe_control_gym/controllers/ppo/ppo.py @@ -15,6 +15,7 @@ import os import time +from gymnasium import spaces import numpy as np import torch @@ -58,8 +59,10 @@ def __init__(self, self.env = env_func() self.env = RecordEpisodeStatistics(self.env) # Agent. - self.agent = PPOAgent(self.env.observation_space, - self.env.action_space, + self.obs_space = spaces.Box(low=np.array([-2,-2,-2,-2,-2,-2,-2,-2]), high=np.array([2,2,2,2,2,2,2,2]), shape=(8,)) + self.act_space = spaces.Box(low=np.array([-0.25,-0.25]), high=np.array([0.25,0.25]), shape=(2,)) + self.agent = PPOAgent(self.obs_space, + self.act_space, hidden_dim=self.hidden_dim, use_clipped_value=self.use_clipped_value, clip_param=self.clip_param, @@ -113,6 +116,7 @@ def reset(self): def close(self): '''Shuts down and cleans up lingering resources.''' + self.firmware_wrapper.close() self.env.close() if self.training: self.eval_env.close() @@ -162,33 +166,9 @@ def learn(self, ): '''Performs learning (pre-training, training, fine-tuning, etc).''' while self.total_steps < self.max_env_steps: - results = self.train_step() - # Checkpoint. - if self.total_steps >= self.max_env_steps or (self.save_interval and self.total_steps % self.save_interval == 0): - # Latest/final checkpoint. - self.save(self.checkpoint_path) - self.logger.info(f'Checkpoint | {self.checkpoint_path}') - if self.num_checkpoints and self.total_steps % (self.max_env_steps // self.num_checkpoints) == 0: - # Intermediate checkpoint. - path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') - self.save(path) - # Evaluation. - if self.eval_interval and self.total_steps % self.eval_interval == 0: - eval_results = self.run(env=self.eval_env, n_episodes=self.eval_batch_size) - results['eval'] = eval_results - self.logger.info('Eval | ep_lengths {:.2f} +/- {:.2f} | ep_return {:.3f} +/- {:.3f}'.format(eval_results['ep_lengths'].mean(), - eval_results['ep_lengths'].std(), - eval_results['ep_returns'].mean(), - eval_results['ep_returns'].std())) - # Save best model. - eval_score = eval_results['ep_returns'].mean() - eval_best_score = getattr(self, 'eval_best_score', -np.infty) - if self.eval_save_best and eval_best_score < eval_score: - self.eval_best_score = eval_score - self.save(os.path.join(self.output_dir, 'model_best.pt')) - # Logging. - if self.log_interval and self.total_steps % self.log_interval == 0: - self.log_step(results) + self.train_step() + + self.save(os.path.join(self.output_dir, 'model_best.pt')) def select_action(self, obs, info=None): '''Determine the action to take at the current timestep. @@ -202,9 +182,12 @@ def select_action(self, obs, info=None): ''' with torch.no_grad(): - obs = torch.FloatTensor(obs).to(self.device) + extended_obs = np.concatenate((np.squeeze(obs), self.X_GOAL[info['current_step'], :])) + obs = torch.FloatTensor(extended_obs).to(self.device) action = self.agent.ac.act(obs) + action = np.clip(np.squeeze([action]), [-1, -1], [1, 1])*0.25 + return action def run(self, @@ -280,65 +263,76 @@ def run(self, def train_step(self): '''Performs a training/fine-tuning step.''' self.agent.train() - self.obs_normalizer.unset_read_only() - rollouts = PPOBuffer(self.env.observation_space, self.env.action_space, self.rollout_steps, self.rollout_batch_size) - obs = self.obs - true_obs = self.true_obs - info = self.info + rollouts = PPOBuffer(self.obs_space, self.act_space, self.rollout_steps, self.rollout_batch_size) + obs, info = self.firmware_wrapper.reset() + obs = np.squeeze(obs.reshape((12, 1))[:4, :]) + firmware_action = np.zeros((4,1)) start = time.time() for _ in range(self.rollout_steps): + extended_obs = np.concatenate((obs, self.X_GOAL[info['current_step']//20, :])) with torch.no_grad(): - action, v, logp = self.agent.ac.step(torch.FloatTensor(obs).to(self.device)) - unsafe_action = action + unsafe_action, v, logp = self.agent.ac.step(torch.FloatTensor(extended_obs).to(self.device)) + + action = np.clip(np.squeeze([unsafe_action]), [-1, -1], [1, 1])*0.25 + scaled_unsafe_action = action # Adding safety filter success = False if self.safety_filter is not None and (self.filter_train_actions is True or self.penalize_sf_diff is True): - physical_action = self.env.envs[0].denormalize_action(action) - unextended_obs = np.squeeze(true_obs)[:self.env.envs[0].symbolic.nx] - certified_action, success = self.safety_filter.certify_action(unextended_obs, physical_action, info) + certified_action, success = self.safety_filter.certify_action(obs, action, info) if success and self.filter_train_actions is True: - action = self.env.envs[0].normalize_action(certified_action) + action = certified_action else: self.safety_filter.ocp_solver.reset() - certified_action, success = self.safety_filter.certify_action(unextended_obs, physical_action, info) + certified_action, success = self.safety_filter.certify_action(obs, action, info) if success and self.filter_train_actions is True: - action = self.env.envs[0].normalize_action(certified_action) + action = certified_action + + + pos = [(action[0] + obs[0]), (action[1] + obs[2]), 1] + vel = [0, 0, 0] + acc = [0, 0, 0] + yaw = 0 + rpy_rate = [0, 0, 0] + args = [pos, vel, acc, yaw, rpy_rate] + curr_time = info['current_step']//20 * self.CTRL_DT + self.firmware_wrapper.sendFullStateCmd(*args, curr_time) + + # Step the environment. + next_obs, rew, done, info, firmware_action = self.firmware_wrapper.step(curr_time, firmware_action) + next_obs = np.squeeze(next_obs.reshape((12, 1))[:4, :]) + rew = self.get_reward(next_obs, info) + if done or info['current_step']//20-1 >= self.X_GOAL.shape[0] - 1 or self.firmware_wrapper._error == True: + next_obs, info = self.env_reset(self.firmware_wrapper) + next_obs = np.squeeze(next_obs.reshape((12, 1))[:4, :]) + firmware_action = np.zeros((4,1)) + mask = 0 + else: + mask = 1 - action = np.atleast_2d(np.squeeze([action])) - next_obs, rew, done, info = self.env.step(action) - if done[0] and self.use_safe_reset: - next_obs, info = self.env_reset(self.env) if self.penalize_sf_diff and success: rew = np.log(rew) - rew -= self.sf_penalty * np.linalg.norm(physical_action - certified_action) + rew -= self.sf_penalty * np.linalg.norm(scaled_unsafe_action - certified_action) rew = np.exp(rew) - next_true_obs = next_obs - next_obs = self.obs_normalizer(next_obs) - rew = self.reward_normalizer(rew, done) - mask = 1 - done.astype(float) + # Time truncation is not the same as true termination. terminal_v = np.zeros_like(v) - for idx, inf in enumerate(info['n']): - if 'terminal_info' not in inf: - continue - inff = inf['terminal_info'] + if 'terminal_info' in info: + inff = info['terminal_info'] if 'TimeLimit.truncated' in inff and inff['TimeLimit.truncated']: - terminal_obs = inf['terminal_observation'] + terminal_obs = info['terminal_observation'] terminal_obs_tensor = torch.FloatTensor(terminal_obs).unsqueeze(0).to(self.device) terminal_val = self.agent.ac.critic(terminal_obs_tensor).squeeze().detach().cpu().numpy() - terminal_v[idx] = terminal_val + terminal_v = terminal_val - rollouts.push({'obs': obs, 'act': unsafe_action, 'rew': rew, 'mask': mask, 'v': v, 'logp': logp, 'terminal_v': terminal_v}) + rollouts.push({'obs': extended_obs, 'act': unsafe_action, 'rew': rew, 'mask': mask, 'v': v, 'logp': logp, 'terminal_v': terminal_v}) obs = next_obs - true_obs = next_true_obs - info = info['n'][0] - self.obs = obs - self.true_obs = true_obs - self.info = info + self.total_steps += self.rollout_batch_size * self.rollout_steps # Learn from rollout batch. - last_val = self.agent.ac.critic(torch.FloatTensor(obs).to(self.device)).detach().cpu().numpy() + extended_obs = np.concatenate((obs, self.X_GOAL[info['current_step']//20, :])) + last_val = self.agent.ac.critic(torch.FloatTensor(extended_obs).to(self.device)).detach().cpu().numpy() + last_val = last_val.reshape((1,1)) ret, adv = compute_returns_and_advantages(rollouts.rew, rollouts.v, rollouts.mask, @@ -351,9 +345,18 @@ def train_step(self): # Prevent divide-by-0 for repetitive tasks. rollouts.adv = (adv - adv.mean()) / (adv.std() + 1e-6) results = self.agent.update(rollouts, self.device) - results.update({'step': self.total_steps, 'elapsed_time': time.time() - start}) + print(f'Steps: {self.total_steps}, Reward: {np.sum(rollouts.rew)}, Time: {time.time() - start}') return results + def get_reward(self, obs, info): + wp_idx = min(info['current_step']//20, self.X_GOAL.shape[0] - 1) # +1 because state has already advanced but counter not incremented. + state_error = obs - self.X_GOAL[wp_idx] + dist = np.sum(np.array([2, 0, 2, 0]) * state_error * state_error) + rew = -dist + rew = np.exp(rew) + + return rew + def log_step(self, results ): @@ -423,7 +426,7 @@ def env_reset(self, env): info (dict): The initial info. ''' success = False - act = self.model.U_EQ + act = np.array([0,0]) obs, info = env.reset() if self.safety_filter is not None: self.safety_filter.reset_before_run() @@ -432,12 +435,10 @@ def env_reset(self, env): while success is not True or np.any(self.safety_filter.slack_prev > 1e-4): obs, info = env.reset() info['current_step'] = 1 - physical_action = self.env.envs[0].denormalize_action(act) - unextended_obs = np.squeeze(obs)[:self.env.envs[0].symbolic.nx] self.safety_filter.reset_before_run() - _, success = self.safety_filter.certify_action(unextended_obs, physical_action, info) + _, success = self.safety_filter.certify_action(np.squeeze(obs.reshape((12, 1))[:4, :]), act, info) if not success: self.safety_filter.ocp_solver.reset() - _, success = self.safety_filter.certify_action(unextended_obs, physical_action, info) + _, success = self.safety_filter.certify_action(np.squeeze(obs.reshape((12, 1))[:4, :]), act, info) return obs, info diff --git a/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py b/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py index ff2ac8f08..b0a29cf20 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py +++ b/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py @@ -816,44 +816,7 @@ def _get_reward(self): Returns: reward (float): The evaluated reward/cost. ''' - # RL cost. - if self.COST == Cost.RL_REWARD: - state = self.state - act = np.asarray(self.current_noisy_physical_action) - act_error = act - self.U_GOAL - # Quadratic costs w.r.t state and action - # TODO: consider using multiple future goal states for cost in tracking - if self.TASK == Task.STABILIZATION: - state_error = state - self.X_GOAL - dist = np.sum(self.rew_state_weight * state_error * state_error) - dist += np.sum(self.rew_act_weight * act_error * act_error) - if self.TASK == Task.TRAJ_TRACKING: - wp_idx = min(self.ctrl_step_counter + 1, self.X_GOAL.shape[0] - 1) # +1 because state has already advanced but counter not incremented. - state_error = state - self.X_GOAL[wp_idx] - dist = np.sum(self.rew_state_weight * state_error * state_error) - dist += np.sum(self.rew_act_weight * act_error * act_error) - rew = -dist - # Convert rew to be positive and bounded [0,1]. - if self.rew_exponential: - rew = np.exp(rew) - return rew - - # Control cost. - if self.COST == Cost.QUADRATIC: - if self.TASK == Task.STABILIZATION: - return float(-1 * self.symbolic.loss(x=self.state, - Xr=self.X_GOAL, - u=self.current_clipped_action, - Ur=self.U_GOAL, - Q=self.Q, - R=self.R)['l']) - if self.TASK == Task.TRAJ_TRACKING: - return float(-1 * self.symbolic.loss(x=self.state, - Xr=self.X_GOAL[self.ctrl_step_counter + 1, :], # +1 because state has already advanced but counter not incremented. - u=self.current_clipped_action, - Ur=self.U_GOAL, - Q=self.Q, - R=self.R)['l']) + return 1 def _get_done(self): '''Computes the conditions for termination of an episode. diff --git a/safe_control_gym/safety_filters/mpsc/mpsc.py b/safe_control_gym/safety_filters/mpsc/mpsc.py index 6c0239b63..666b754fc 100644 --- a/safe_control_gym/safety_filters/mpsc/mpsc.py +++ b/safe_control_gym/safety_filters/mpsc/mpsc.py @@ -187,6 +187,9 @@ def solve_casadi_optimization(self, action (ndarray): The certified action. feasible (bool): Whether the safety filtering was feasible or not. ''' + obs = obs.reshape((-1, 1)) + uncertified_action = uncertified_action.reshape((-1, 1)) + opti_dict = self.opti_dict opti = opti_dict['opti'] z_var = opti_dict['z_var']