From a9f2084fd3525973da8bd820b59db8afbe3b8271 Mon Sep 17 00:00:00 2001 From: Federico-PizarroBejarano Date: Wed, 4 Oct 2023 11:57:59 -0400 Subject: [PATCH] Adding various minor updates --- examples/mpsc/train_rl_model.sh | 4 +- .../cartpole/cartpole_stab.yaml | 19 ++- .../cartpole/cartpole_track.yaml | 17 +- .../cartpole/ppo_cartpole.yaml | 33 ++-- .../cartpole/sac_cartpole.yaml | 29 ++-- .../cartpole/safe_explorer_ppo_cartpole.yaml | 47 ++++++ .../safe_explorer_ppo_cartpole_pretrain.yaml | 54 +++++++ .../quadrotor_2D/ppo_quadrotor_2D.yaml | 10 +- .../quadrotor_2D/quadrotor_2D_stab.yaml | 29 ++-- .../quadrotor_2D/quadrotor_2D_track.yaml | 27 ++-- .../quadrotor_2D/sac_quadrotor_2D.yaml | 10 +- .../safe_explorer_ppo_quadrotor_2D.yaml | 33 ++++ ...fe_explorer_ppo_quadrotor_2D_pretrain.yaml | 40 +++++ .../quadrotor_3D/ppo_quadrotor_3D.yaml | 12 +- .../quadrotor_3D/quadrotor_3D_stab.yaml | 51 +++--- .../quadrotor_3D/quadrotor_3D_track.yaml | 49 +++--- .../quadrotor_3D/sac_quadrotor_3D.yaml | 12 +- .../safe_explorer_ppo_quadrotor_3D.yaml | 33 ++++ ...fe_explorer_ppo_quadrotor_3D_pretrain.yaml | 40 +++++ .../rl/models/ppo/ppo_model_cartpole_stab.pt | Bin 153194 -> 150891 bytes .../models/ppo/ppo_model_quadrotor_2D_stab.pt | Bin 479082 -> 464235 bytes .../rl/models/sac/sac_model_cartpole_stab.pt | Bin 257970 -> 252775 bytes .../models/sac/sac_model_quadrotor_2D_stab.pt | Bin 917426 -> 888067 bytes .../safe_explorer_ppo_model_cartpole_stab.pt | Bin 0 -> 203437 bytes .../safe_explorer_ppo_model_cartpole_track.pt | Bin 0 -> 234339 bytes ...fe_explorer_ppo_model_quadrotor_2D_stab.pt | Bin 0 -> 667141 bytes ...e_explorer_ppo_model_quadrotor_2D_track.pt | Bin 0 -> 796411 bytes examples/rl/rl_experiment.py | 9 +- examples/rl/rl_experiment.sh | 4 +- examples/rl/train_rl_model.sh | 32 +++- .../controllers/base_controller.py | 4 +- safe_control_gym/controllers/ddpg/ddpg.py | 3 - .../controllers/ddpg/ddpg_utils.py | 8 +- safe_control_gym/controllers/ppo/ppo.py | 3 +- safe_control_gym/controllers/ppo/ppo.yaml | 9 +- safe_control_gym/controllers/ppo/ppo_utils.py | 4 +- safe_control_gym/controllers/rarl/rap.py | 6 - safe_control_gym/controllers/rarl/rarl.py | 4 - safe_control_gym/controllers/sac/sac.py | 19 +-- safe_control_gym/controllers/sac/sac.yaml | 1 + safe_control_gym/controllers/sac/sac_utils.py | 17 +- .../safe_explorer/safe_explorer_utils.py | 2 +- .../controllers/safe_explorer/safe_ppo.py | 35 +++-- safe_control_gym/envs/benchmark_env.py | 11 +- safe_control_gym/envs/gym_control/cartpole.py | 2 +- .../envs/gym_pybullet_drones/quadrotor.py | 11 +- .../experiments/base_experiment.py | 5 +- .../experiments/execute_rl_controller.py | 148 ------------------ .../experiments/train_rl_controller.py | 90 +++++++++++ safe_control_gym/safety_filters/cbf/cbf.py | 2 + safe_control_gym/safety_filters/mpsc/mpsc.py | 13 +- .../mpsc/mpsc_cost_function/abstract_cost.py | 16 ++ safe_control_gym/utils/plotting.py | 3 +- tests/test_examples/test_rl.py | 4 +- 54 files changed, 652 insertions(+), 362 deletions(-) create mode 100644 examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole.yaml create mode 100644 examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole_pretrain.yaml create mode 100644 examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D.yaml create mode 100644 examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D_pretrain.yaml create mode 100644 examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D.yaml create mode 100644 examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D_pretrain.yaml create mode 100644 examples/rl/models/safe_explorer_ppo/safe_explorer_ppo_model_cartpole_stab.pt create mode 100644 examples/rl/models/safe_explorer_ppo/safe_explorer_ppo_model_cartpole_track.pt create mode 100644 examples/rl/models/safe_explorer_ppo/safe_explorer_ppo_model_quadrotor_2D_stab.pt create mode 100644 examples/rl/models/safe_explorer_ppo/safe_explorer_ppo_model_quadrotor_2D_track.pt delete mode 100644 safe_control_gym/experiments/execute_rl_controller.py create mode 100644 safe_control_gym/experiments/train_rl_controller.py diff --git a/examples/mpsc/train_rl_model.sh b/examples/mpsc/train_rl_model.sh index 66e9bcab2..f1ac98b6a 100755 --- a/examples/mpsc/train_rl_model.sh +++ b/examples/mpsc/train_rl_model.sh @@ -13,7 +13,7 @@ ALGO='ppo' rm -r -f ./unsafe_rl_temp_data/ # Train the unsafe controller/agent. -python3 ../../safe_control_gym/experiments/execute_rl_controller.py \ +python3 ../../safe_control_gym/experiments/train_rl_controller.py \ --algo ${ALGO} \ --task ${SYS} \ --overrides \ @@ -24,7 +24,7 @@ python3 ../../safe_control_gym/experiments/execute_rl_controller.py \ --seed 2 # Move the newly trained unsafe model. -mv ./unsafe_rl_temp_data/seed2_*/model_latest.pt ./models/${ALGO}_model_${SYS}_${TASK}.pt +mv ./unsafe_rl_temp_data/seed2_*/model_best.pt ./models/${ALGO}_model_${SYS}_${TASK}.pt # Removed the temporary data used to train the new unsafe model. rm -r -f ./unsafe_rl_temp_data/ diff --git a/examples/rl/config_overrides/cartpole/cartpole_stab.yaml b/examples/rl/config_overrides/cartpole/cartpole_stab.yaml index c35781864..1f3a0e430 100644 --- a/examples/rl/config_overrides/cartpole/cartpole_stab.yaml +++ b/examples/rl/config_overrides/cartpole/cartpole_stab.yaml @@ -7,26 +7,31 @@ task_config: normalized_rl_action_space: True # state initialization + init_state: + init_x: 0.1 + init_x_dot: -1.5 + init_theta: -0.155 + init_theta_dot: 0.75 randomized_init: True randomized_inertial_prop: False init_state_randomization_info: init_x: distrib: 'uniform' - low: -1 - high: 1 + low: -2 + high: 2 init_x_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -2 + high: 2 init_theta: distrib: 'uniform' low: -0.16 high: 0.16 init_theta_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 task: stabilization task_info: @@ -40,7 +45,7 @@ task_config: episode_len_sec: 10 cost: rl_reward - obs_goal_horizon: 1 + obs_goal_horizon: 0 # RL Reward rew_state_weight: [1, 1, 1, 1] diff --git a/examples/rl/config_overrides/cartpole/cartpole_track.yaml b/examples/rl/config_overrides/cartpole/cartpole_track.yaml index 2b4e6a9c0..c889b2c6a 100644 --- a/examples/rl/config_overrides/cartpole/cartpole_track.yaml +++ b/examples/rl/config_overrides/cartpole/cartpole_track.yaml @@ -7,26 +7,31 @@ task_config: normalized_rl_action_space: True # state initialization + init_state: + init_x: 0 + init_x_dot: 0 + init_theta: 0 + init_theta_dot: 0 randomized_init: True randomized_inertial_prop: False init_state_randomization_info: init_x: distrib: 'uniform' - low: -1 - high: 1 + low: -2 + high: 2 init_x_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -2 + high: 2 init_theta: distrib: 'uniform' low: -0.16 high: 0.16 init_theta_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 task: traj_tracking task_info: diff --git a/examples/rl/config_overrides/cartpole/ppo_cartpole.yaml b/examples/rl/config_overrides/cartpole/ppo_cartpole.yaml index 838c16d5c..0dc45648b 100644 --- a/examples/rl/config_overrides/cartpole/ppo_cartpole.yaml +++ b/examples/rl/config_overrides/cartpole/ppo_cartpole.yaml @@ -1,40 +1,41 @@ algo: ppo algo_config: # model args - hidden_dim: 64 + hidden_dim: 32 + activation: 'leaky_relu' norm_obs: False norm_reward: False clip_obs: 10.0 clip_reward: 10.0 # loss args - gamma: 0.99 - use_gae: True - gae_lambda: 0.95 + gamma: 0.98 + use_gae: False + gae_lambda: 0.8 use_clipped_value: False - clip_param: 0.2 - target_kl: 0.01 - entropy_coef: 0.01 + clip_param: 0.1 + target_kl: 1.587713889686473e-07 + entropy_coef: 0.00010753631441212628 # optim args - opt_epochs: 10 - mini_batch_size: 64 - actor_lr: 0.0003 - critic_lr: 0.001 + 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 + max_env_steps: 300000 num_workers: 1 rollout_batch_size: 4 - rollout_steps: 100 + rollout_steps: 150 deque_size: 10 eval_batch_size: 10 # misc - log_interval: 1000 - save_interval: 1000 + log_interval: 6000 + save_interval: 0 num_checkpoints: 0 - eval_interval: 1000 + eval_interval: 6000 eval_save_best: True tensorboard: False diff --git a/examples/rl/config_overrides/cartpole/sac_cartpole.yaml b/examples/rl/config_overrides/cartpole/sac_cartpole.yaml index fc36a8d61..0e6b4a406 100644 --- a/examples/rl/config_overrides/cartpole/sac_cartpole.yaml +++ b/examples/rl/config_overrides/cartpole/sac_cartpole.yaml @@ -1,19 +1,26 @@ algo: sac algo_config: # model args - hidden_dim: 64 + hidden_dim: 256 + activation: 'relu' + + # loss args + gamma: 0.98 + tau: 0.12145208815621376 + init_temperature: 0.2 use_entropy_tuning: False + target_entropy: null # optim args train_interval: 100 - train_batch_size: 64 - actor_lr: 0.001 - critic_lr: 0.001 + train_batch_size: 512 + actor_lr: 0.00045196308120485273 + critic_lr: 0.022547326782152065 entropy_lr: 0.001 # runner args - max_env_steps: 100000 - warm_up_steps: 1000 + max_env_steps: 150000 + warm_up_steps: 100 rollout_batch_size: 4 num_workers: 1 max_buffer_size: 1000000 @@ -21,9 +28,9 @@ algo_config: eval_batch_size: 10 # misc - log_interval: 1000 - save_interval: 1000 - num_checkpoints: 100 - eval_interval: 1000 + log_interval: 3000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 3000 eval_save_best: True - tensorboard: True + tensorboard: False diff --git a/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole.yaml b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole.yaml new file mode 100644 index 000000000..1bbd445fa --- /dev/null +++ b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole.yaml @@ -0,0 +1,47 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 32 + activation: 'leaky_relu' + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # Safety layer args + pretraining: False + pretrained: null + constraint_slack: [0.01, 0.01, 0.01, 0.05, 0.01, 0.01, 0.01, 0.05] + constraint_hidden_dim: 100 + + # 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: 300000 + num_workers: 1 + rollout_batch_size: 4 + rollout_steps: 150 + 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 diff --git a/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole_pretrain.yaml b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole_pretrain.yaml new file mode 100644 index 000000000..1f11d6022 --- /dev/null +++ b/examples/rl/config_overrides/cartpole/safe_explorer_ppo_cartpole_pretrain.yaml @@ -0,0 +1,54 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 32 + activation: 'leaky_relu' + norm_obs: False + norm_reward: False + clip_obs: 10.0 + clip_reward: 10.0 + + # Safety layer args + pretraining: True + pretrained: null + constraint_hidden_dim: 100 + constraint_lr: 0.0001 + constraint_batch_size: 256 + constraint_steps_per_epoch: 3000 + constraint_epochs: 200 + constraint_eval_steps: 1500 + constraint_eval_interval: 5 + constraint_buffer_size: 1000000 + constraint_slack: [0.01, 0.01, 0.01, 0.05, 0.01, 0.01, 0.01, 0.05] + + # 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: 4 + rollout_steps: 150 + deque_size: 10 + eval_batch_size: 10 + + # misc + log_interval: 100 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 100 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml b/examples/rl/config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml index dab8c09c2..de1a4a42f 100644 --- a/examples/rl/config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml +++ b/examples/rl/config_overrides/quadrotor_2D/ppo_quadrotor_2D.yaml @@ -19,9 +19,9 @@ algo_config: rollout_steps: 1000 # misc - log_interval: 1000 - save_interval: 1000 - num_checkpoints: 10 - eval_interval: 1000 + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 eval_save_best: True - tensorboard: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml index 80597efcb..a30ded73b 100644 --- a/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml +++ b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_stab.yaml @@ -7,34 +7,41 @@ task_config: quad_type: 2 normalized_rl_action_space: True + init_state: + init_x: 0.5 + init_x_dot: 0 + init_z: 1.5 + init_z_dot: 0 + init_theta: 0 + init_theta_dot: 0 randomized_init: True randomized_inertial_prop: False init_state_randomization_info: init_x: distrib: 'uniform' - low: -1 - high: 1 + low: -2 + high: 2 init_x_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_z: distrib: 'uniform' - low: 1 + low: 0.3 # Just so it doesn't crash into the ground high: 2 init_z_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_theta: distrib: 'uniform' low: -0.2 high: 0.2 init_theta_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1.5 + high: 1.5 task: stabilization task_info: @@ -45,9 +52,9 @@ task_config: M: 0.027 Iyy: 1.4e-05 - episode_len_sec: 6 + episode_len_sec: 5 cost: rl_reward - obs_goal_horizon: 1 + obs_goal_horizon: 0 # RL Reward rew_state_weight: [1, 1, 1, 1, 1, 1] diff --git a/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml index 75418678b..2aa366fb5 100644 --- a/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml +++ b/examples/rl/config_overrides/quadrotor_2D/quadrotor_2D_track.yaml @@ -7,34 +7,41 @@ task_config: quad_type: 2 normalized_rl_action_space: True + init_state: + init_x: 0 + init_x_dot: 0 + init_z: 1 + init_z_dot: 0 + init_theta: 0 + init_theta_dot: 0 randomized_init: True randomized_inertial_prop: False init_state_randomization_info: init_x: distrib: 'uniform' - low: -1 - high: 1 + low: -2 + high: 2 init_x_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_z: distrib: 'uniform' - low: 1 + low: 0.3 # Just so it doesn't crash into the ground high: 2 init_z_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_theta: distrib: 'uniform' low: -0.2 high: 0.2 init_theta_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1.5 + high: 1.5 task: traj_tracking task_info: @@ -48,7 +55,7 @@ task_config: M: 0.027 Iyy: 1.4e-05 - episode_len_sec: 6 + episode_len_sec: 5 cost: rl_reward obs_goal_horizon: 1 diff --git a/examples/rl/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml b/examples/rl/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml index db5891195..74e58b4a6 100644 --- a/examples/rl/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml +++ b/examples/rl/config_overrides/quadrotor_2D/sac_quadrotor_2D.yaml @@ -21,9 +21,9 @@ algo_config: eval_batch_size: 10 # misc - log_interval: 1000 - save_interval: 1000 - num_checkpoints: 10 - eval_interval: 1000 + log_interval: 4000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 4000 eval_save_best: True - tensorboard: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D.yaml b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D.yaml new file mode 100644 index 000000000..20c0afdde --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D.yaml @@ -0,0 +1,33 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: True + entropy_coef: 0.01 + + # Safety layer args + pretraining: False + pretrained: null + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01] + constraint_hidden_dim: 150 + + # optim args + opt_epochs: 20 + mini_batch_size: 250 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 500000 + rollout_batch_size: 4 + rollout_steps: 250 + + # misc + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D_pretrain.yaml b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D_pretrain.yaml new file mode 100644 index 000000000..cf6be1c48 --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_2D/safe_explorer_ppo_quadrotor_2D_pretrain.yaml @@ -0,0 +1,40 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: True + entropy_coef: 0.01 + + # Safety layer args + pretraining: True + pretrained: null + constraint_hidden_dim: 150 + constraint_lr: 0.0001 + constraint_batch_size: 256 + constraint_steps_per_epoch: 3000 + constraint_epochs: 500 + constraint_eval_steps: 1500 + constraint_eval_interval: 5 + constraint_buffer_size: 1000000 + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01] + + # optim args + opt_epochs: 20 + mini_batch_size: 250 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 500000 + rollout_batch_size: 4 + rollout_steps: 250 + + # misc + log_interval: 100 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 100 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/ppo_quadrotor_3D.yaml b/examples/rl/config_overrides/quadrotor_3D/ppo_quadrotor_3D.yaml index dab8c09c2..e94a7da1c 100644 --- a/examples/rl/config_overrides/quadrotor_3D/ppo_quadrotor_3D.yaml +++ b/examples/rl/config_overrides/quadrotor_3D/ppo_quadrotor_3D.yaml @@ -14,14 +14,14 @@ algo_config: critic_lr: 0.001 # runner args - max_env_steps: 500000 + max_env_steps: 1000000 rollout_batch_size: 4 rollout_steps: 1000 # misc - log_interval: 1000 - save_interval: 1000 - num_checkpoints: 10 - eval_interval: 1000 + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 eval_save_best: True - tensorboard: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml index 4a30bd46f..79736724c 100644 --- a/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml +++ b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_stab.yaml @@ -7,34 +7,47 @@ task_config: quad_type: 3 normalized_rl_action_space: True + init_state: + init_x: 0.4 + init_x_dot: 0 + init_y: 0.4 + init_y_dot: 0 + init_z: 1.4 + init_z_dot: 0 + init_phi: 0 + init_theta: 0 + init_psi: 0 + init_p: 0 + init_q: 0 + init_r: 0 randomized_init: True randomized_inertial_prop: False init_state_randomization_info: init_x: distrib: 'uniform' - low: -1 - high: 1 + low: -2 + high: 2 init_x_dot: - distrib: 'uniform' - low: -0.1 - high: 0.1 - init_y: distrib: 'uniform' low: -1 high: 1 + init_y: + distrib: 'uniform' + low: -2 + high: 2 init_y_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_z: distrib: 'uniform' - low: 1 + low: 0.3 # Just so it doesn't crash into the ground high: 2 init_z_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_phi: distrib: 'uniform' low: -0.2 @@ -49,16 +62,16 @@ task_config: high: 0.2 init_p: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_q: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_r: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 task: stabilization task_info: @@ -71,9 +84,9 @@ task_config: Iyy: 1.4e-5 Izz: 2.17e-5 - episode_len_sec: 6 + episode_len_sec: 5 cost: rl_reward - obs_goal_horizon: 1 + obs_goal_horizon: 0 # RL Reward rew_state_weight: [1, 0.01, 1, 0.01, 1, 0.01, 0.5, 0.5, 0.5, 0.01, 0.01, 0.01] diff --git a/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_track.yaml b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_track.yaml index 71cd5a46a..813d94a74 100644 --- a/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_track.yaml +++ b/examples/rl/config_overrides/quadrotor_3D/quadrotor_3D_track.yaml @@ -7,34 +7,47 @@ task_config: quad_type: 3 normalized_rl_action_space: True + init_state: + init_x: 0.4 + init_x_dot: 0 + init_y: 0.4 + init_y_dot: 0 + init_z: 1.4 + init_z_dot: 0 + init_phi: 0 + init_theta: 0 + init_psi: 0 + init_p: 0 + init_q: 0 + init_r: 0 randomized_init: True randomized_inertial_prop: False init_state_randomization_info: init_x: distrib: 'uniform' - low: -1 - high: 1 + low: -2 + high: 2 init_x_dot: - distrib: 'uniform' - low: -0.1 - high: 0.1 - init_y: distrib: 'uniform' low: -1 high: 1 + init_y: + distrib: 'uniform' + low: -2 + high: 2 init_y_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_z: distrib: 'uniform' - low: 1 + low: 0.3 # Just so it doesn't crash into the ground high: 2 init_z_dot: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_phi: distrib: 'uniform' low: -0.2 @@ -49,16 +62,16 @@ task_config: high: 0.2 init_p: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_q: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 init_r: distrib: 'uniform' - low: -0.1 - high: 0.1 + low: -1 + high: 1 task: traj_tracking task_info: @@ -76,7 +89,7 @@ task_config: Iyy: 1.4e-5 Izz: 2.17e-5 - episode_len_sec: 6 + episode_len_sec: 5 cost: rl_reward obs_goal_horizon: 1 diff --git a/examples/rl/config_overrides/quadrotor_3D/sac_quadrotor_3D.yaml b/examples/rl/config_overrides/quadrotor_3D/sac_quadrotor_3D.yaml index db5891195..0ee2b2325 100644 --- a/examples/rl/config_overrides/quadrotor_3D/sac_quadrotor_3D.yaml +++ b/examples/rl/config_overrides/quadrotor_3D/sac_quadrotor_3D.yaml @@ -12,7 +12,7 @@ algo_config: entropy_lr: 0.001 # runner args - max_env_steps: 200000 + max_env_steps: 400000 warm_up_steps: 1000 rollout_batch_size: 4 num_workers: 1 @@ -21,9 +21,9 @@ algo_config: eval_batch_size: 10 # misc - log_interval: 1000 - save_interval: 1000 - num_checkpoints: 10 - eval_interval: 1000 + log_interval: 4000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 4000 eval_save_best: True - tensorboard: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D.yaml b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D.yaml new file mode 100644 index 000000000..980be22cf --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D.yaml @@ -0,0 +1,33 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: False + entropy_coef: 0.01 + + # Safety layer args + pretraining: False + pretrained: null + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01] + constraint_hidden_dim: 150 + + # optim args + opt_epochs: 20 + mini_batch_size: 256 + actor_lr: 0.001 + critic_lr: 0.001 + + # runner args + max_env_steps: 1000000 + rollout_batch_size: 4 + rollout_steps: 1000 + + # misc + log_interval: 10000 + save_interval: 0 + num_checkpoints: 0 + eval_interval: 10000 + eval_save_best: True + tensorboard: False diff --git a/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D_pretrain.yaml b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D_pretrain.yaml new file mode 100644 index 000000000..994243f3f --- /dev/null +++ b/examples/rl/config_overrides/quadrotor_3D/safe_explorer_ppo_quadrotor_3D_pretrain.yaml @@ -0,0 +1,40 @@ +algo: safe_explorer_ppo +algo_config: + # model args + hidden_dim: 128 + + # loss args + use_gae: False + entropy_coef: 0.01 + + # Safety layer args + pretraining: True + pretrained: null + constraint_hidden_dim: 150 + constraint_lr: 0.0001 + constraint_batch_size: 256 + constraint_steps_per_epoch: 3000 + constraint_epochs: 500 + constraint_eval_steps: 1500 + constraint_eval_interval: 5 + constraint_buffer_size: 1000000 + constraint_slack: [0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 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curr_path='.'): '''Main function to run RL experiments. Args: - gui (bool): Whether to display the gui and plot graphs. + gui (bool): Whether to display the gui. + plot (bool): Whether to plot graphs. n_episodes (int): The number of episodes to execute. n_steps (int): How many steps to run the experiment. curr_path (str): The current relative path to the experiment folder. @@ -40,7 +41,7 @@ def run(gui=True, n_episodes=1, n_steps=None, curr_path='.'): env_func = partial(make, config.task, **config.task_config) - env = env_func() + env = env_func(gui=gui) # Setup controller. ctrl = make(config.algo, @@ -59,7 +60,7 @@ def run(gui=True, n_episodes=1, n_steps=None, curr_path='.'): results, metrics = experiment.run_evaluation(n_episodes=n_episodes, n_steps=n_steps) ctrl.close() - if gui is True: + if plot is True: if system == Environment.CARTPOLE: graph1_1 = 2 graph1_2 = 3 diff --git a/examples/rl/rl_experiment.sh b/examples/rl/rl_experiment.sh index 322338c58..203bc14e4 100755 --- a/examples/rl/rl_experiment.sh +++ b/examples/rl/rl_experiment.sh @@ -9,6 +9,7 @@ TASK='track' ALGO='ppo' # ALGO='sac' +# ALGO='safe_explorer_ppo' if [ "$SYS" == 'cartpole' ]; then SYS_NAME=$SYS @@ -24,4 +25,5 @@ python3 ./rl_experiment.py \ ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ ./config_overrides/${SYS}/${ALGO}_${SYS}.yaml \ --kv_overrides \ - algo_config.training=False + algo_config.training=False \ + task_config.randomized_init=False diff --git a/examples/rl/train_rl_model.sh b/examples/rl/train_rl_model.sh index 6d20e1d39..fabd643e7 100755 --- a/examples/rl/train_rl_model.sh +++ b/examples/rl/train_rl_model.sh @@ -9,6 +9,7 @@ TASK='stab' ALGO='ppo' # ALGO='sac' +# ALGO='safe_explorer_ppo' if [ "$SYS" == 'cartpole' ]; then SYS_NAME=$SYS @@ -19,8 +20,29 @@ fi # Removed the temporary data used to train the new unsafe model. rm -r -f ./unsafe_rl_temp_data/ +if [ "$ALGO" == 'safe_explorer_ppo' ]; then + # Pretrain the unsafe controller/agent. + python3 ../../safe_control_gym/experiments/train_rl_controller.py \ + --algo ${ALGO} \ + --task ${SYS_NAME} \ + --overrides \ + ./config_overrides/${SYS}/${ALGO}_${SYS}_pretrain.yaml \ + ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ + --output_dir ./ \ + --tag unsafe_rl_temp_data/ \ + --seed 2 \ + --kv_overrides \ + task_config.init_state=None + + # Move the newly trained unsafe model. + mv ./unsafe_rl_temp_data/seed2_*/model_latest.pt ./models/${ALGO}/${ALGO}_pretrain_${SYS}_${TASK}.pt + + # Removed the temporary data used to train the new unsafe model. + rm -r -f ./unsafe_rl_temp_data/ +fi + # Train the unsafe controller/agent. -python3 ../../safe_control_gym/experiments/execute_rl_controller.py \ +python3 ../../safe_control_gym/experiments/train_rl_controller.py \ --algo ${ALGO} \ --task ${SYS_NAME} \ --overrides \ @@ -28,10 +50,14 @@ python3 ../../safe_control_gym/experiments/execute_rl_controller.py \ ./config_overrides/${SYS}/${SYS}_${TASK}.yaml \ --output_dir ./ \ --tag unsafe_rl_temp_data/ \ - --seed 2 + --seed 2 \ + --kv_overrides \ + task_config.init_state=None \ + task_config.randomized_init=True \ + algo_config.pretrained=./models/${ALGO}/${ALGO}_pretrain_${SYS}_${TASK}.pt # Move the newly trained unsafe model. -mv ./unsafe_rl_temp_data/seed2_*/model_latest.pt ./models/${ALGO}/${ALGO}_model_${SYS}_${TASK}.pt +mv ./unsafe_rl_temp_data/seed2_*/model_best.pt ./models/${ALGO}/${ALGO}_model_${SYS}_${TASK}.pt # Removed the temporary data used to train the new unsafe model. rm -r -f ./unsafe_rl_temp_data/ diff --git a/safe_control_gym/controllers/base_controller.py b/safe_control_gym/controllers/base_controller.py index 80c5f82d8..dafd5e666 100644 --- a/safe_control_gym/controllers/base_controller.py +++ b/safe_control_gym/controllers/base_controller.py @@ -150,9 +150,9 @@ def get_prior(self, env, prior_info={}): prior_prop_rand_info: {} and to ensure the resulting config.algo_config contains both the params - from ctrl config and prior config, chain them to the --overrides like:: + from ctrl config and prior config, chain them to the --overrides like: - python safe_control_gym/experiments/execute_rl_controller.py --algo mpc --task quadrotor --overrides base.yaml mpc.yaml prior.yaml ... + python experiment.py --algo mpc --task quadrotor --overrides base.yaml mpc.yaml prior.yaml ... Also note we look for prior_info from the incoming function arg first, then the ctrl itself. this allows changing the prior model during learning by calling:: diff --git a/safe_control_gym/controllers/ddpg/ddpg.py b/safe_control_gym/controllers/ddpg/ddpg.py index a85ead7dd..022d93e66 100644 --- a/safe_control_gym/controllers/ddpg/ddpg.py +++ b/safe_control_gym/controllers/ddpg/ddpg.py @@ -4,9 +4,6 @@ * [Continuous Control with Deep Reinforcement Learning](https://arxiv.org/pdf/1509.02971.pdf) * [openai spinning up - ddpg](https://github.com/openai/spinningup/tree/master/spinup/algos/pytorch/ddpg) * [DeepRL - ddpg](https://github.com/ShangtongZhang/DeepRL/blob/master/deep_rl/agent/DDPG_agent.py) - -Example: - $ python safe_control_gym/experiments/execute_rl_controller.py --algo ddpg --task cartpole --output_dir results --tag test/cartpole_ddpg --seed 6 ''' import os diff --git a/safe_control_gym/controllers/ddpg/ddpg_utils.py b/safe_control_gym/controllers/ddpg/ddpg_utils.py index ae23091b0..eaed162df 100644 --- a/safe_control_gym/controllers/ddpg/ddpg_utils.py +++ b/safe_control_gym/controllers/ddpg/ddpg_utils.py @@ -97,7 +97,7 @@ def compute_q_loss(self, batch): def update(self, batch): '''Updates model parameters based on current training batch.''' - resutls = defaultdict(list) + results = defaultdict(list) # actor update policy_loss = self.compute_policy_loss(batch) @@ -114,9 +114,9 @@ def update(self, batch): # update target networks soft_update(self.ac, self.ac_targ, self.tau) - resutls['policy_loss'] = policy_loss.item() - resutls['critic_loss'] = critic_loss.item() - return resutls + results['policy_loss'] = policy_loss.item() + results['critic_loss'] = critic_loss.item() + return results # ----------------------------------------------------------------------------------- diff --git a/safe_control_gym/controllers/ppo/ppo.py b/safe_control_gym/controllers/ppo/ppo.py index 0d0b8f7fb..80ec0dc52 100644 --- a/safe_control_gym/controllers/ppo/ppo.py +++ b/safe_control_gym/controllers/ppo/ppo.py @@ -65,7 +65,8 @@ def __init__(self, actor_lr=self.actor_lr, critic_lr=self.critic_lr, opt_epochs=self.opt_epochs, - mini_batch_size=self.mini_batch_size) + mini_batch_size=self.mini_batch_size, + activation=self.activation) self.agent.to(self.device) # Pre-/post-processing. self.obs_normalizer = BaseNormalizer() diff --git a/safe_control_gym/controllers/ppo/ppo.yaml b/safe_control_gym/controllers/ppo/ppo.yaml index 043010d06..04126e91f 100644 --- a/safe_control_gym/controllers/ppo/ppo.yaml +++ b/safe_control_gym/controllers/ppo/ppo.yaml @@ -1,13 +1,14 @@ # Model args hidden_dim: 64 +activation: 'tanh' norm_obs: False norm_reward: False -clip_obs: 10. -clip_reward: 10. +clip_obs: 10 +clip_reward: 10 # Loss args -gamma: 0.99 -use_gae: False +gamma: 0.99 +use_gae: False gae_lambda: 0.95 use_clipped_value: False clip_param: 0.2 diff --git a/safe_control_gym/controllers/ppo/ppo_utils.py b/safe_control_gym/controllers/ppo/ppo_utils.py index 24ead5b4a..e08df5f76 100644 --- a/safe_control_gym/controllers/ppo/ppo_utils.py +++ b/safe_control_gym/controllers/ppo/ppo_utils.py @@ -27,6 +27,7 @@ def __init__(self, critic_lr=0.001, opt_epochs=10, mini_batch_size=64, + activation='tanh', **kwargs ): # Parameters. @@ -38,11 +39,12 @@ def __init__(self, self.entropy_coef = entropy_coef self.opt_epochs = opt_epochs self.mini_batch_size = mini_batch_size + self.activation = activation # Model. self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, - activation='tanh') + activation=self.activation) # Optimizers. self.actor_opt = torch.optim.Adam(self.ac.actor.parameters(), actor_lr) self.critic_opt = torch.optim.Adam(self.ac.critic.parameters(), critic_lr) diff --git a/safe_control_gym/controllers/rarl/rap.py b/safe_control_gym/controllers/rarl/rap.py index 738d8ff9f..10b28262d 100644 --- a/safe_control_gym/controllers/rarl/rap.py +++ b/safe_control_gym/controllers/rarl/rap.py @@ -6,12 +6,6 @@ * [robust-adversarial-rl](https://github.com/jerinphilip/robust-adversarial-rl) * [rllab-adv](https://github.com/lerrel/rllab-adv) * [Robust Reinforcement Learning via adversary pools](https://github.com/eugenevinitsky/robust_RL_multi_adversary) - -Example: - train on cartpole_adversary: - $ python safe_control_gym/experiments/execute_rl_controller.py --mode train_two_phase --exp_id rap_cartpole_adv \ - --algo rap --task cartpole_adversary --num_workers 2 --max_env_steps 2000000 \ - --tensorboard --use_gae --num_adversaries 2 ''' import os diff --git a/safe_control_gym/controllers/rarl/rarl.py b/safe_control_gym/controllers/rarl/rarl.py index d1a96183a..637f0b43d 100644 --- a/safe_control_gym/controllers/rarl/rarl.py +++ b/safe_control_gym/controllers/rarl/rarl.py @@ -6,10 +6,6 @@ * [robust-adversarial-rl](https://github.com/jerinphilip/robust-adversarial-rl) * [rllab-adv](https://github.com/lerrel/rllab-adv) * [Robust Reinforcement Learning via adversary pools](https://github.com/eugenevinitsky/robust_RL_multi_adversary) - -Example: - train on cartpole with disturbance: - $ python safe_control_gym/experiments/execute_rl_controller.py --algo rarl --task cartpole --overrides benchmark/configs/overrides/rarl_cartpole.yaml ''' import os diff --git a/safe_control_gym/controllers/sac/sac.py b/safe_control_gym/controllers/sac/sac.py index edeb64abc..8f4da20c2 100644 --- a/safe_control_gym/controllers/sac/sac.py +++ b/safe_control_gym/controllers/sac/sac.py @@ -69,7 +69,8 @@ def __init__(self, target_entropy=self.target_entropy, actor_lr=self.actor_lr, critic_lr=self.critic_lr, - entropy_lr=self.entropy_lr) + entropy_lr=self.entropy_lr, + activation=self.activation) self.agent.to(self.device) # pre-/post-processing @@ -117,7 +118,7 @@ def close(self): self.eval_env.close() self.logger.close() - def save(self, path, save_buffer=True): + def save(self, path, save_buffer=False): '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) os.makedirs(path_dir, exist_ok=True) @@ -168,12 +169,12 @@ def learn(self, env=None, **kwargs): # 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.save(self.checkpoint_path, save_buffer=False) 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, save_buffer=False) + self.save(path, save_buffer=True) # eval if self.eval_interval and self.total_steps % self.eval_interval == 0: @@ -188,7 +189,7 @@ def learn(self, env=None, **kwargs): 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')) + self.save(os.path.join(self.output_dir, 'model_best.pt'), save_buffer=False) # logging if self.log_interval and self.total_steps % self.log_interval == 0: @@ -267,11 +268,11 @@ def train_step(self, **kwargs): start = time.time() if self.total_steps < self.warm_up_steps: - act = np.stack([self.env.action_space.sample() for _ in range(self.rollout_batch_size)]) + action = np.stack([self.env.action_space.sample() for _ in range(self.rollout_batch_size)]) else: with torch.no_grad(): - act = self.agent.ac.act(torch.FloatTensor(obs).to(self.device), deterministic=False) - next_obs, rew, done, info = self.env.step(act) + action = self.agent.ac.act(torch.FloatTensor(obs).to(self.device), deterministic=False) + next_obs, rew, done, info = self.env.step(action) next_obs = self.obs_normalizer(next_obs) rew = self.reward_normalizer(rew, done) @@ -299,7 +300,7 @@ def train_step(self, **kwargs): self.buffer.push({ 'obs': obs, - 'act': act, + 'act': action, 'rew': rew, # 'next_obs': next_obs, # 'mask': mask, diff --git a/safe_control_gym/controllers/sac/sac.yaml b/safe_control_gym/controllers/sac/sac.yaml index 740b4b8c0..6cecd6667 100644 --- a/safe_control_gym/controllers/sac/sac.yaml +++ b/safe_control_gym/controllers/sac/sac.yaml @@ -1,5 +1,6 @@ # model args hidden_dim: 256 +activation: 'relu' norm_obs: False norm_reward: False clip_obs: 10. diff --git a/safe_control_gym/controllers/sac/sac_utils.py b/safe_control_gym/controllers/sac/sac_utils.py index 09bd9a862..46531904d 100644 --- a/safe_control_gym/controllers/sac/sac_utils.py +++ b/safe_control_gym/controllers/sac/sac_utils.py @@ -32,6 +32,7 @@ def __init__(self, actor_lr=0.001, critic_lr=0.001, entropy_lr=0.001, + activation='relu', **kwargs): # params self.obs_space = obs_space @@ -41,8 +42,10 @@ def __init__(self, self.tau = tau self.use_entropy_tuning = use_entropy_tuning + self.activation = activation + # model - self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, activation='relu') + self.ac = MLPActorCritic(obs_space, act_space, hidden_dims=[hidden_dim] * 2, activation=self.activation) self.log_alpha = torch.tensor(np.log(init_temperature)) if self.use_entropy_tuning: @@ -139,7 +142,7 @@ def compute_q_loss(self, batch): def update(self, batch): '''Updates model parameters based on current training batch.''' - resutls = defaultdict(list) + results = defaultdict(list) # actor update policy_loss, entropy_loss = self.compute_policy_loss(batch) @@ -161,10 +164,10 @@ def update(self, batch): # update target networks soft_update(self.ac, self.ac_targ, self.tau) - resutls['policy_loss'] = policy_loss.item() - resutls['critic_loss'] = critic_loss.item() - resutls['entropy_loss'] = entropy_loss.item() - return resutls + results['policy_loss'] = policy_loss.item() + results['critic_loss'] = critic_loss.item() + results['entropy_loss'] = entropy_loss.item() + return results # ----------------------------------------------------------------------------------- @@ -287,7 +290,7 @@ def unscale_fn(x): # Rescale action from [-1, 1] to [low, high] def act(self, obs, deterministic=False): a, _ = self.actor(obs, deterministic, False) - return a.numpy() + return a.cpu().numpy() # ----------------------------------------------------------------------------------- diff --git a/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py b/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py index d8899a186..533d10b61 100644 --- a/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py +++ b/safe_control_gym/controllers/safe_explorer/safe_explorer_utils.py @@ -46,7 +46,7 @@ def __init__(self, # Constraint slack variables/values. assert slack is not None and isinstance(slack, (int, float, list)) if isinstance(slack, (int, float)): - slack = [slack] * obs_space.shape[0] + slack = [slack] * self.num_constraints self.slack = np.array(slack) # Optimizers. self.optimizers = [ diff --git a/safe_control_gym/controllers/safe_explorer/safe_ppo.py b/safe_control_gym/controllers/safe_explorer/safe_ppo.py index e378d1de3..89f694451 100644 --- a/safe_control_gym/controllers/safe_explorer/safe_ppo.py +++ b/safe_control_gym/controllers/safe_explorer/safe_ppo.py @@ -44,12 +44,12 @@ def __init__(self, self.env = VecRecordEpisodeStatistics(self.env, self.deque_size) self.eval_env = env_func(seed=seed * 111) self.eval_env = RecordEpisodeStatistics(self.eval_env, self.deque_size) - self.num_constraints = self.env.envs[0].num_constraints + self.num_constraints = self.eval_env.constraints.num_state_constraints else: # Testing only. self.env = env_func() self.env = RecordEpisodeStatistics(self.env) - self.num_constraints = self.env.num_constraints + self.num_constraints = self.env.constraints.num_state_constraints # Safety layer. self.safety_layer = SafetyLayer(self.env.observation_space, self.env.action_space, @@ -111,7 +111,7 @@ def reset(self): self.total_steps = 0 obs, info = self.env.reset() self.obs = self.obs_normalizer(obs) - self.c = np.array([inf['constraint_values'] for inf in info['n']]) + self.c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) else: # Add episodic stats to be tracked. self.env.add_tracker('constraint_violation', 0, mode='queue') @@ -126,7 +126,8 @@ def close(self): self.logger.close() def save(self, - path + path, + save_buffer=False ): '''Saves model params and experiment state to checkpoint path.''' path_dir = os.path.dirname(path) @@ -137,7 +138,7 @@ def save(self, 'obs_normalizer': self.obs_normalizer.state_dict(), 'reward_normalizer': self.reward_normalizer.state_dict(), } - if self.training: + if self.training and save_buffer: exp_state = { 'total_steps': self.total_steps, 'obs': self.obs, @@ -187,12 +188,12 @@ def learn(self, # Checkpoint. if self.total_steps >= final_step or (self.save_interval and self.total_steps % self.save_interval == 0): # Latest/final checkpoint. - self.save(self.checkpoint_path) + self.save(self.checkpoint_path, save_buffer=False) self.logger.info(f'Checkpoint | {self.checkpoint_path}') if self.num_checkpoints and self.total_steps % (final_step // self.num_checkpoints) == 0: # Intermediate checkpoint. path = os.path.join(self.output_dir, 'checkpoints', f'model_{self.total_steps}.pt') - self.save(path) + self.save(path, save_buffer=True) # Evaluation. if self.eval_interval and self.total_steps % self.eval_interval == 0: if self.pretraining: @@ -210,7 +211,7 @@ def learn(self, 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')) + self.save(os.path.join(self.output_dir, 'model_best.pt'), save_buffer=False) # Logging. if self.log_interval and self.total_steps % self.log_interval == 0: self.log_step(results) @@ -340,7 +341,7 @@ def train_step(self): 'c': c, }) obs = next_obs - c = np.array([inf['constraint_values'] for inf in info['n']]) + c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) self.obs = obs self.c = c self.total_steps += self.rollout_batch_size * self.rollout_steps @@ -435,7 +436,7 @@ def collect_constraint_data(self, step = 0 obs, info = self.env.reset() obs = self.obs_normalizer(obs) - c = np.array([inf['constraint_values'] for inf in info['n']]) + c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) while step < num_steps: action_spaces = self.env.get_attr('action_space') action = np.array([space.sample() for space in action_spaces]) @@ -444,19 +445,19 @@ def collect_constraint_data(self, c_next = [] for i, d in enumerate(done): if d: - c_next_i = info['n'][i]['terminal_info']['constraint_values'] + c_next_i = info['n'][i]['terminal_info']['constraint_values'][:self.num_constraints] else: - c_next_i = info['n'][i]['constraint_values'] + c_next_i = info['n'][i]['constraint_values'][:self.num_constraints] c_next.append(c_next_i) c_next = np.array(c_next) self.constraint_buffer.push({'act': action, 'obs': obs, 'c': c, 'c_next': c_next}) obs = obs_next - c = np.array([inf['constraint_values'] for inf in info['n']]) + c = np.array([inf['constraint_values'][:self.num_constraints] for inf in info['n']]) step += self.rollout_batch_size def eval_constraint_models(self): '''Runs evaluation for the constraint models.''' - eval_resutls = defaultdict(list) + eval_results = defaultdict(list) self.safety_layer.eval() self.obs_normalizer.set_read_only() # Collect evaluation data. @@ -464,7 +465,7 @@ def eval_constraint_models(self): for batch in self.constraint_buffer.sampler(self.constraint_batch_size): losses = self.safety_layer.compute_loss(batch) for i, loss in enumerate(losses): - eval_resutls[f'constraint_{i}_loss'].append(loss.item()) + eval_results[f'constraint_{i}_loss'].append(loss.item()) self.constraint_buffer.reset() - eval_resutls = {k: sum(v) / len(v) for k, v in eval_resutls.items()} - return eval_resutls + eval_results = {k: sum(v) / len(v) for k, v in eval_results.items()} + return eval_results diff --git a/safe_control_gym/envs/benchmark_env.py b/safe_control_gym/envs/benchmark_env.py index d06bbaab8..c60220974 100644 --- a/safe_control_gym/envs/benchmark_env.py +++ b/safe_control_gym/envs/benchmark_env.py @@ -436,7 +436,7 @@ def before_step(self, action): # Sanity check (reset at least once). self._check_initial_reset() # Save the raw input action. - action = np.atleast_1d(action) + action = np.atleast_1d(np.squeeze(action)) if action.ndim != 1: raise ValueError('[ERROR]: The action returned by the controller must be 1 dimensional.') @@ -511,8 +511,13 @@ def after_step(self, obs, rew, done, info): # Apply penalized reward when close to constraint violation if self.COST == Cost.RL_REWARD: - if self.constraints is not None and self.use_constraint_penalty and self.constraints.is_almost_active(self, c_value=c_value): - rew += self.constraint_penalty + if self.constraints is not None and self.use_constraint_penalty and self.constraints.is_violated(self, c_value=c_value): + if self.rew_exponential: + rew = np.log(rew) + rew += self.constraint_penalty + rew = np.exp(rew) + else: + rew += self.constraint_penalty # Terminate when reaching time limit, # but distinguish between done due to true termination or time limit reached diff --git a/safe_control_gym/envs/gym_control/cartpole.py b/safe_control_gym/envs/gym_control/cartpole.py index 56f4cfaaf..842ceca1c 100644 --- a/safe_control_gym/envs/gym_control/cartpole.py +++ b/safe_control_gym/envs/gym_control/cartpole.py @@ -645,7 +645,7 @@ def _get_done(self): done (bool): Whether an episode is over. ''' # Done if goal reached for stabilization task with quadratic cost. - if self.TASK == Task.STABILIZATION and self.COST == Cost.QUADRATIC: + if self.TASK == Task.STABILIZATION: self.goal_reached = bool(np.linalg.norm(self.state - self.X_GOAL) < self.TASK_INFO['stabilization_goal_tolerance']) if self.goal_reached: return True diff --git a/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py b/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py index 9042ed3de..b13ac4901 100644 --- a/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py +++ b/safe_control_gym/envs/gym_pybullet_drones/quadrotor.py @@ -864,20 +864,11 @@ def _get_done(self): done (bool): Whether an episode is over. ''' # Done if goal reached for stabilization task with quadratic cost. - if self.TASK == Task.STABILIZATION and self.COST == Cost.QUADRATIC: + if self.TASK == Task.STABILIZATION: self.goal_reached = bool(np.linalg.norm(self.state - self.X_GOAL) < self.TASK_INFO['stabilization_goal_tolerance']) if self.goal_reached: return True - # # Done if the episode length is exceeded. - # if (self.ctrl_step_counter + 1) / self.CTRL_FREQ >= self.EPISODE_LEN_SEC: - # return True - - # # Done if a constraint is violated. - # if self.constraints is not None: - # if self.DONE_ON_VIOLATION and self.constraints.is_violated(self): - # return True - # Done if state is out-of-bounds. if self.done_on_out_of_bound: if self.QUAD_TYPE == QuadType.ONE_D: diff --git a/safe_control_gym/experiments/base_experiment.py b/safe_control_gym/experiments/base_experiment.py index f41de3df5..1005a9982 100644 --- a/safe_control_gym/experiments/base_experiment.py +++ b/safe_control_gym/experiments/base_experiment.py @@ -161,9 +161,8 @@ def _select_action(self, obs, info): if self.safety_filter is not None: physical_action = self.env.denormalize_action(action) unextended_obs = obs[:self.env.symbolic.nx] - certified_action, success = self.safety_filter.certify_action(unextended_obs, physical_action, info) - if success: - action = self.env.normalize_action(certified_action) + certified_action, _ = self.safety_filter.certify_action(unextended_obs, physical_action, info) + action = self.env.normalize_action(certified_action) return action diff --git a/safe_control_gym/experiments/execute_rl_controller.py b/safe_control_gym/experiments/execute_rl_controller.py deleted file mode 100644 index abfdea4f4..000000000 --- a/safe_control_gym/experiments/execute_rl_controller.py +++ /dev/null @@ -1,148 +0,0 @@ -'''Template training/plotting/testing script.''' - -import os -import pickle -from functools import partial - -import torch - -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, save_video, - set_device_from_config, - set_dir_from_config, - set_seed_from_config) - - -def train(config): - '''Training template. - - Usage: - * to start training, use with `--func train`. - * to restore from a previous training, additionally use `--restore {dir_path}` - where `dir_path` is the output folder from previous training. - ''' - # Experiment setup. - if not config.restore: - set_dir_from_config(config) - set_seed_from_config(config) - set_device_from_config(config) - # Define function to create task/env. - env_func = partial(make, config.task, output_dir=config.output_dir, **config.task_config) - # Create the controller/control_agent. - control_agent = make(config.algo, - env_func, - training=True, - checkpoint_path=os.path.join(config.output_dir, 'model_latest.pt'), - output_dir=config.output_dir, - use_gpu=config.use_gpu, - seed=config.seed, - **config.algo_config) - control_agent.reset() - if config.restore: - control_agent.load(os.path.join(config.restore, 'model_latest.pt')) - # Training. - control_agent.learn() - control_agent.close() - print('Training done.') - - -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.') - - -def test_policy(config): - '''Run the (trained) policy/controller for evaluation. - - Usage - * use with `--func test`. - * to test policy from a trained model checkpoint, additionally use - `--restore {dir_path}` where `dir_path` is folder to the trained model. - * to test un-trained policy (e.g. non-learning based), use as it is. - ''' - # Evaluation setup. - set_device_from_config(config) - if config.set_test_seed: - # seed the evaluation (both controller and env) if given - set_seed_from_config(config) - env_seed = config.seed - else: - env_seed = None - # Define function to create task/env. - env_func = partial(make, config.task, seed=env_seed, output_dir=config.output_dir, **config.task_config) - # Create the controller/control_agent. - control_agent = make(config.algo, - env_func, - training=False, - checkpoint_path=os.path.join(config.output_dir, 'model_latest.pt'), - output_dir=config.output_dir, - use_gpu=config.use_gpu, - seed=config.seed, - **config.algo_config) - control_agent.reset() - if config.restore: - control_agent.load(os.path.join(config.restore, 'model_latest.pt')) - # Test controller. - results = control_agent.run(n_episodes=config.algo_config.eval_batch_size, - render=config.render, - verbose=config.verbose, - use_adv=config.use_adv) - # Save evalution results. - if config.eval_output_dir is not None and config.eval_output_dir: - eval_output_dir = config.eval_output_dir - else: - eval_output_dir = os.path.join(config.output_dir, 'eval') - os.makedirs(eval_output_dir, exist_ok=True) - # test trajs and statistics - eval_path = os.path.join(eval_output_dir, config.eval_output_path) - os.makedirs(os.path.dirname(eval_path), exist_ok=True) - with open(eval_path, 'wb') as f: - pickle.dump(results, f) - ep_lengths = results['ep_lengths'] - ep_returns = results['ep_returns'] - msg = 'eval_ep_length {:.2f} +/- {:.2f}\n'.format(ep_lengths.mean(), ep_lengths.std()) - msg += 'eval_ep_return {:.3f} +/- {:.3f}\n'.format(ep_returns.mean(), ep_returns.std()) - print(msg) - if 'frames' in results: - save_video(os.path.join(eval_output_dir, 'video.gif'), results['frames']) - control_agent.close() - print('Evaluation done.') - - -MAIN_FUNCS = {'train': train, 'plot': make_plots, 'test': test_policy} - - -if __name__ == '__main__': - # Make config. - fac = ConfigFactory() - fac.add_argument('--func', type=str, default='train', help='main function to run.') - fac.add_argument('--thread', type=int, default=0, help='number of threads to use (set by torch).') - fac.add_argument('--render', action='store_true', help='if to render in policy test.') - fac.add_argument('--verbose', action='store_true', help='if to print states & actions in policy test.') - fac.add_argument('--use_adv', action='store_true', help='if to evaluate against adversary.') - fac.add_argument('--set_test_seed', action='store_true', help='if to set seed when testing policy.') - fac.add_argument('--eval_output_dir', type=str, default='', help='folder path to save evaluation results.') - fac.add_argument('--eval_output_path', type=str, default='test_results.pkl', help='file path to save evaluation results.') - config = fac.merge() - # System settings. - if config.thread > 0: - # E.g. set single thread for less context switching - torch.set_num_threads(config.thread) - # Execute. - func = MAIN_FUNCS.get(config.func, None) - if func is None: - raise Exception(f'Main function {config.func} not supported.') - func(config) diff --git a/safe_control_gym/experiments/train_rl_controller.py b/safe_control_gym/experiments/train_rl_controller.py new file mode 100644 index 000000000..095e326be --- /dev/null +++ b/safe_control_gym/experiments/train_rl_controller.py @@ -0,0 +1,90 @@ +'''Template training/plotting/testing script.''' + +import os +from functools import partial + +import torch + +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_dir_from_config, + set_seed_from_config) + + +def train(config): + '''Training template. + + Usage: + * to restore from a previous training, additionally use `--restore {dir_path}` + where `dir_path` is the output folder from previous training. + ''' + # Experiment setup. + if not config.restore: + set_dir_from_config(config) + + set_seed_from_config(config) + set_device_from_config(config) + + # Define function to create task/env. + env_func = partial(make, + config.task, + output_dir=config.output_dir, + **config.task_config + ) + + # Create the controller/control_agent. + control_agent = make(config.algo, + env_func, + training=True, + checkpoint_path=os.path.join(config.output_dir, 'model_latest.pt'), + output_dir=config.output_dir, + use_gpu=config.use_gpu, + seed=config.seed, + **config.algo_config) + control_agent.reset() + + if config.restore: + control_agent.load(os.path.join(config.restore, 'model_latest.pt')) + + # Training. + control_agent.learn() + control_agent.close() + print('Training done.') + + +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.') + + +MAIN_FUNCS = {'train': train, 'plot': make_plots} + + +if __name__ == '__main__': + # Make config. + fac = ConfigFactory() + fac.add_argument('--func', type=str, default='train', help='main function to run.') + fac.add_argument('--thread', type=int, default=0, help='number of threads to use (set by torch).') + config_dict = fac.merge() + + # System settings. + if config_dict.thread > 0: + # E.g. set single thread for less context switching + torch.set_num_threads(config_dict.thread) + + func = MAIN_FUNCS.get(config_dict.func, None) + if func is None: + raise Exception(f'Main function {config_dict.func} not supported.') + func(config_dict) diff --git a/safe_control_gym/safety_filters/cbf/cbf.py b/safe_control_gym/safety_filters/cbf/cbf.py index 64cd7c162..4fa2eae8a 100644 --- a/safe_control_gym/safety_filters/cbf/cbf.py +++ b/safe_control_gym/safety_filters/cbf/cbf.py @@ -233,9 +233,11 @@ def certify_action(self, success (bool): Whether the safety filtering was successful or not. ''' + uncertified_action = np.clip(uncertified_action, self.env.physical_action_bounds[0], self.env.physical_action_bounds[1]) self.results_dict['uncertified_action'].append(uncertified_action) certified_action, success = self.solve_optimization(current_state, uncertified_action) self.results_dict['feasible'].append(success) + certified_action = np.squeeze(np.array(certified_action)) self.results_dict['certified_action'].append(certified_action) self.results_dict['correction'].append(np.linalg.norm(certified_action - uncertified_action)) diff --git a/safe_control_gym/safety_filters/mpsc/mpsc.py b/safe_control_gym/safety_filters/mpsc/mpsc.py index 7395df1cd..97c0d98df 100644 --- a/safe_control_gym/safety_filters/mpsc/mpsc.py +++ b/safe_control_gym/safety_filters/mpsc/mpsc.py @@ -35,7 +35,7 @@ def __init__(self, warmstart: bool = True, additional_constraints: list = None, use_terminal_set: bool = True, - cost_function: str = Cost_Function.ONE_STEP_COST, + cost_function: Cost_Function = Cost_Function.ONE_STEP_COST, **kwargs ): '''Initialize the MPSC. @@ -49,7 +49,7 @@ def __init__(self, warmstart (bool): If the previous MPC soln should be used to warmstart the next mpc step. additional_constraints (list): List of additional constraints to consider. use_terminal_set (bool): Whether to use a terminal set constraint or not. - cost_function (str): A string (from Cost_Function) representing the cost function to be used. + cost_function (Cost_Function): A string (from Cost_Function) representing the cost function to be used. ''' # Store all params/args. @@ -59,8 +59,10 @@ def __init__(self, super().__init__(env_func, **kwargs) + np.random.seed(self.seed) + # Setup the Environments. - self.env = env_func(randomized_init=False) + self.env = env_func(normalized_rl_action_space=False) self.training_env = env_func(randomized_init=True, init_state=None, cost='quadratic', @@ -163,7 +165,7 @@ def solve_optimization(self, self.prev_action = next_u_val feasible = True except Exception as e: - print('Error Return Status: ', opti.debug.return_status()) + print('Error Return Status:', opti.debug.return_status()) print(e) feasible = False action = None @@ -185,7 +187,7 @@ def certify_action(self, certified_action (ndarray): The certified action success (bool): Whether the safety filtering was successful or not. ''' - + uncertified_action = np.clip(uncertified_action, self.env.physical_action_bounds[0], self.env.physical_action_bounds[1]) self.results_dict['uncertified_action'].append(uncertified_action) success = True @@ -219,6 +221,7 @@ def certify_action(self, success = False certified_action = clipped_action + certified_action = np.squeeze(np.array(certified_action)) self.results_dict['kinf'].append(self.kinf) self.results_dict['certified_action'].append(certified_action) self.results_dict['correction'].append(np.linalg.norm(certified_action - uncertified_action)) diff --git a/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/abstract_cost.py b/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/abstract_cost.py index 0352feb3b..3980790c2 100644 --- a/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/abstract_cost.py +++ b/safe_control_gym/safety_filters/mpsc/mpsc_cost_function/abstract_cost.py @@ -2,10 +2,26 @@ from abc import ABC, abstractmethod +from safe_control_gym.envs.benchmark_env import BenchmarkEnv + class MPSC_COST(ABC): '''Abstract MPSC Cost Function to be extended for different cost functions.''' + def __init__(self, + env: BenchmarkEnv = None, + ): + '''Initialize the MPSC Cost. + + Args: + env (BenchmarkEnv): Environment for the task. + ''' + + self.env = env + + # Setup attributes. + self.model = self.env.symbolic if env is not None else None + @abstractmethod def get_cost(self, opti_dict): '''Returns the cost function for the MPSC optimization in symbolic form. diff --git a/safe_control_gym/utils/plotting.py b/safe_control_gym/utils/plotting.py index 17daa6f49..6c275a707 100644 --- a/safe_control_gym/utils/plotting.py +++ b/safe_control_gym/utils/plotting.py @@ -183,7 +183,8 @@ def plot_from_logs(src_dir, out_dir, window=None, keys=None): src_dir (str): folder to read logs. out_dir (str): folder to save figures. window (int): window size for smoothing. - keys (list): specify name of stats to plot, None means plot all.''' + keys (list): specify name of stats to plot, None means plot all. + ''' # Find all logs. log_files = [] for r, _, f in os.walk(src_dir): diff --git a/tests/test_examples/test_rl.py b/tests/test_examples/test_rl.py index 8e0bfd713..81ec07198 100644 --- a/tests/test_examples/test_rl.py +++ b/tests/test_examples/test_rl.py @@ -7,7 +7,7 @@ @pytest.mark.parametrize('SYS', ['cartpole', 'quadrotor_2D', 'quadrotor_3D']) @pytest.mark.parametrize('TASK', ['stab', 'track']) -@pytest.mark.parametrize('ALGO', ['ppo', 'sac']) +@pytest.mark.parametrize('ALGO', ['ppo', 'sac', 'safe_explorer_ppo']) def test_rl(SYS, TASK, ALGO): SYS_NAME = 'quadrotor' if 'quadrotor' in SYS else SYS sys.argv[1:] = ['--algo', ALGO, @@ -18,4 +18,4 @@ def test_rl(SYS, TASK, ALGO): '--kv_overrides', 'algo_config.training=False', ] - run(gui=False, n_episodes=None, n_steps=10, curr_path='./examples/rl') + run(gui=False, plot=False, n_episodes=None, n_steps=10, curr_path='./examples/rl')

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