From 839c76fd096145356bd4a33aaf9c8639e01e46da Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Mon, 2 Oct 2023 08:22:39 +0300 Subject: [PATCH 01/22] init script for ant --- scripts/ant/create_dataset.py | 78 +++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 scripts/ant/create_dataset.py diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py new file mode 100644 index 0000000..d2ce03b --- /dev/null +++ b/scripts/ant/create_dataset.py @@ -0,0 +1,78 @@ +__authors__ = ["Kallinteris Andreas"] +import gymnasium as gym +import minari +assert minari.__version__ == "0.4.1" +import numpy as np +from minari import DataCollectorV0, StepDataCallback +from stable_baselines3 import A2C, PPO, SAC, TD3 +import stable_baselines3 +assert stable-baselines3.__version__ == "2.0.0a5" + + + +SEED = 12345 +NUM_STEPS = int(10e6) +POLICY_NOISE = 0.1 + +class AddExcludedObservationElements(StepDataCallback): + """Add Excluded observation elements like cfrc_ext to the observation space.""" + def __call__(self, env, **kwargs): + step_data = super().__call__(env, **kwargs) + # if getattr(env, "_include_cinert_in_observation", None) is False: + # if getattr(env, "_include_cvel_in_observation ", None) is False: + # if getattr(env, "_include_qfrc_actuator_in_observation ", None) is False: + if env.unwrapped._include_cfrc_ext_in_observation is False: + step_data["observations"] = np.concatenate([step_data["observations"], env.unwrapped.contact_forces[1:].flat.copy()]) + + + return step_data + +dataset_name = "Ant-v5-6_7k_return-v0" +dataset = None + +# Check if dataset already exist +assert dataset_name not in minari.list_local_datasets() + +# Create Environment +env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1e9) +# add callback to add cfrc_ext to the obs space +collector_env = DataCollectorV0(env, step_data_callback=AddExcludedObservationElements, record_infos=True) +# we do not observe `cfrc_ext` with the agent, but we keep it in the dataset observations +collector_env.dataset_observation_space = gym.spaces.Box(-np.inf, np.inf, (105,), np.float64) +obs, _ = collector_env.reset(seed=SEED) + +# load policy model +# model is from: https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/tree/main/results/ant_v5_without_ctn_SAC/run_9 +model = SAC.load( + path="./results/ant_v5_without_ctn_SAC/run_9/best_model.zip", + env=env, + device="cpu", +) + + +for n_step in range(NUM_STEPS): + action, _ = model.predict(obs, deterministic=True) + # Add some noise to each step action + action += np.random.randn(*action.shape) * POLICY_NOISE + action = action.clip(-1, 1) + + obs, rew, terminated, truncated, info = collector_env.step(action) + # Checkpoint + if (n_step + 1) % 200e3 == 0: + print(f"STEPS RECORDED: {n_step}") + if dataset is None: + dataset = minari.create_dataset_from_collector_env( + collector_env=collector_env, + dataset_id=dataset_name, + algorithm_name="SB3/SAC", + code_permalink="https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/blob/main/create_dataset.py", + author="Kallinteris Andreas", + author_email="kallinteris@protonmail.com", + ) + dataset.update_dataset_from_collector_env(collector_env) + + if terminated or truncated: + env.reset() + +# dataset.update_dataset_from_collector_env(collector_env) +collector_env.save_to_disk("test.hdf5") From c4b9aeeadc4f621a93a00c5ca93ad94f5351ab1d Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Mon, 2 Oct 2023 08:51:26 +0300 Subject: [PATCH 02/22] add model --- scripts/ant/best_model.zip | Bin 0 -> 3379908 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 scripts/ant/best_model.zip diff --git a/scripts/ant/best_model.zip b/scripts/ant/best_model.zip new file mode 100644 index 0000000000000000000000000000000000000000..be1e9b5d42e407953fc0edd52b816653a5a36921 GIT binary patch literal 3379908 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zNEi?z9^~30aey#N5*sb_4+|0xPWP2|!9daU>0Dh$NJK5Ers*xkn;shz6eu=xZK;J8 zxnjgarijg*dBRv>teNz_;dT$sH616MH2zu<)p#mx`=+_$@Q$r&_}{w$QsWJ6OUlA= zIJT1dAKeAL@Vw1RMY*=H%edAt{6C2z!{74%m;c(GJ6ia^ST;!fU-SRJxB>jJk^eVu z0Mj|8`SJE~NIP5N$2Ms@RaD7h+nEwS)@NI+7dP~@;oHuV`1Mp#^#k_5m@2-H)4bN} z5b4Or1rKQ}bD?#rNcx`P$EiYrLrL?0%lq%CBGT}YPhPjvQPP1{8g;nn#|_(DU-t7C z#sr0lB7$P9{JxjW(#+hM(H#d6VHwC;eMis>5*oVIEksA zn!V<~nAWhnr~}W9w&YZ8}RN2{8>06Zr>Nnpp|-4D~GZ zJYC%N+&vr|T-?3xe$-k8`3JqsH>b8~9JP#X(9Z?6%0mgbgL%BFhm z@sg0p2&!jgX2G%H5Sb$fOks4(FdHMC(Pc^OdscK_!CspujndhvH6-#+4(CWjhKT~=e}=2L zd)H`6QYW4?c5MZ5*F?Vj0&Sj9Iv1O@F(qUfix0}XlT=NfG`5;0QaXwJ&GP!ZfM}6K z6!4!Zv^8P=U4}${zjUi#7&JeY;%B Date: Tue, 17 Oct 2023 11:19:34 +0300 Subject: [PATCH 03/22] `Ant` update dataset name --- scripts/ant/create_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index d2ce03b..3f56e33 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -27,7 +27,7 @@ def __call__(self, env, **kwargs): return step_data -dataset_name = "Ant-v5-6_7k_return-v0" +dataset_name = "ant-v5-expert-v0" dataset = None # Check if dataset already exist From e8fdf2c9b6a20de783dc8fd5f270dc0351a5f23c Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Tue, 17 Oct 2023 13:55:29 +0300 Subject: [PATCH 04/22] typo fix --- scripts/ant/create_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index 3f56e33..61a1077 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -6,7 +6,7 @@ from minari import DataCollectorV0, StepDataCallback from stable_baselines3 import A2C, PPO, SAC, TD3 import stable_baselines3 -assert stable-baselines3.__version__ == "2.0.0a5" +assert stable_baselines3.__version__ == "2.0.0a5" From 1b5e5f54aff1edf3fcb8e6bef756f32c7935874b Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Wed, 18 Oct 2023 09:46:47 +0300 Subject: [PATCH 05/22] update --- scripts/ant/create_dataset.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index 61a1077..8b2ef8d 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -60,19 +60,19 @@ def __call__(self, env, **kwargs): # Checkpoint if (n_step + 1) % 200e3 == 0: print(f"STEPS RECORDED: {n_step}") - if dataset is None: - dataset = minari.create_dataset_from_collector_env( - collector_env=collector_env, - dataset_id=dataset_name, - algorithm_name="SB3/SAC", - code_permalink="https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/blob/main/create_dataset.py", - author="Kallinteris Andreas", - author_email="kallinteris@protonmail.com", - ) - dataset.update_dataset_from_collector_env(collector_env) + #if dataset is None: + #dataset.update_dataset_from_collector_env(collector_env) if terminated or truncated: env.reset() # dataset.update_dataset_from_collector_env(collector_env) +dataset = minari.create_dataset_from_collector_env( + collector_env=collector_env, + dataset_id=dataset_name, + algorithm_name="SB3/SAC", + code_permalink="https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/blob/main/create_dataset.py", + author="Kallinteris Andreas", + author_email="kallinteris@protonmail.com", +) collector_env.save_to_disk("test.hdf5") From a3e3bde648e2dff5653b52525f6ca5293ac6fe0e Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Wed, 25 Oct 2023 12:30:04 +0300 Subject: [PATCH 06/22] remove noise and cleanup --- scripts/ant/create_dataset.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index 8b2ef8d..0d5c18b 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -1,4 +1,4 @@ -__authors__ = ["Kallinteris Andreas"] +__credits__ = ["Kallinteris Andreas"] import gymnasium as gym import minari assert minari.__version__ == "0.4.1" @@ -12,7 +12,6 @@ SEED = 12345 NUM_STEPS = int(10e6) -POLICY_NOISE = 0.1 class AddExcludedObservationElements(StepDataCallback): """Add Excluded observation elements like cfrc_ext to the observation space.""" @@ -52,16 +51,11 @@ def __call__(self, env, **kwargs): for n_step in range(NUM_STEPS): action, _ = model.predict(obs, deterministic=True) - # Add some noise to each step action - action += np.random.randn(*action.shape) * POLICY_NOISE - action = action.clip(-1, 1) obs, rew, terminated, truncated, info = collector_env.step(action) # Checkpoint if (n_step + 1) % 200e3 == 0: print(f"STEPS RECORDED: {n_step}") - #if dataset is None: - #dataset.update_dataset_from_collector_env(collector_env) if terminated or truncated: env.reset() @@ -75,4 +69,3 @@ def __call__(self, env, **kwargs): author="Kallinteris Andreas", author_email="kallinteris@protonmail.com", ) -collector_env.save_to_disk("test.hdf5") From ac7a3ff80fa091246dcef46be76468a6f2aa00fd Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Sun, 29 Oct 2023 15:26:41 +0200 Subject: [PATCH 07/22] enable episode truncation --- scripts/ant/create_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index 0d5c18b..9d7ea93 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -33,7 +33,7 @@ def __call__(self, env, **kwargs): assert dataset_name not in minari.list_local_datasets() # Create Environment -env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1e9) +env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1e3) # add callback to add cfrc_ext to the obs space collector_env = DataCollectorV0(env, step_data_callback=AddExcludedObservationElements, record_infos=True) # we do not observe `cfrc_ext` with the agent, but we keep it in the dataset observations From 464c3ec4273d41bba2ceb32f8dda11cec83ac133 Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Wed, 13 Mar 2024 14:32:52 +0200 Subject: [PATCH 08/22] Update create_dataset.py --- scripts/ant/create_dataset.py | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index 9d7ea93..c9660fc 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -1,17 +1,19 @@ __credits__ = ["Kallinteris Andreas"] + import gymnasium as gym import minari -assert minari.__version__ == "0.4.1" import numpy as np -from minari import DataCollectorV0, StepDataCallback -from stable_baselines3 import A2C, PPO, SAC, TD3 import stable_baselines3 -assert stable_baselines3.__version__ == "2.0.0a5" +from minari import DataCollector, StepDataCallback +from stable_baselines3 import A2C, PPO, SAC, TD3 +from envs import ant_v5_release +from gymnasium.wrappers import TransformReward, PassiveEnvChecker, OrderEnforcing, TimeLimit SEED = 12345 -NUM_STEPS = int(10e6) +NUM_STEPS = int(2e6) + class AddExcludedObservationElements(StepDataCallback): """Add Excluded observation elements like cfrc_ext to the observation space.""" @@ -23,21 +25,21 @@ def __call__(self, env, **kwargs): if env.unwrapped._include_cfrc_ext_in_observation is False: step_data["observations"] = np.concatenate([step_data["observations"], env.unwrapped.contact_forces[1:].flat.copy()]) - return step_data -dataset_name = "ant-v5-expert-v0" + +DATASET_NAME = "ant-v5-expert-tuned-v0" dataset = None # Check if dataset already exist -assert dataset_name not in minari.list_local_datasets() +assert DATASET_NAME not in minari.list_local_datasets() # Create Environment -env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1e3) +#env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1000) +env = TimeLimit(OrderEnforcing(PassiveEnvChecker(ant_v5_release.AntEnv(include_cfrc_ext_in_observation=False))), max_episode_steps=1000) # add callback to add cfrc_ext to the obs space -collector_env = DataCollectorV0(env, step_data_callback=AddExcludedObservationElements, record_infos=True) +collector_env = DataCollector(env, step_data_callback=AddExcludedObservationElements, record_infos=False, action_space=env.action_space, observation_space=gym.spaces.Box(-np.inf, np.inf, (105,), np.float64)) # we do not observe `cfrc_ext` with the agent, but we keep it in the dataset observations -collector_env.dataset_observation_space = gym.spaces.Box(-np.inf, np.inf, (105,), np.float64) obs, _ = collector_env.reset(seed=SEED) # load policy model @@ -60,10 +62,9 @@ def __call__(self, env, **kwargs): if terminated or truncated: env.reset() -# dataset.update_dataset_from_collector_env(collector_env) -dataset = minari.create_dataset_from_collector_env( +dataset = minari.create_dataset( collector_env=collector_env, - dataset_id=dataset_name, + dataset_id=DATASET_NAME, algorithm_name="SB3/SAC", code_permalink="https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/blob/main/create_dataset.py", author="Kallinteris Andreas", From 33befbb80ba13e38c4d17f9fe8f2969fe176e220 Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Thu, 21 Mar 2024 19:08:09 +0200 Subject: [PATCH 09/22] typo fix --- scripts/ant/create_dataset.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index c9660fc..0ae64bf 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -14,7 +14,6 @@ SEED = 12345 NUM_STEPS = int(2e6) - class AddExcludedObservationElements(StepDataCallback): """Add Excluded observation elements like cfrc_ext to the observation space.""" def __call__(self, env, **kwargs): @@ -62,11 +61,12 @@ def __call__(self, env, **kwargs): if terminated or truncated: env.reset() -dataset = minari.create_dataset( - collector_env=collector_env, +dataset = collector_env.create_dataset( + # collector_env=collector_env, dataset_id=DATASET_NAME, algorithm_name="SB3/SAC", code_permalink="https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/blob/main/create_dataset.py", author="Kallinteris Andreas", author_email="kallinteris@protonmail.com", + # env_spec ) From 22de9479df84ca0a71f30044fa7ea6598e5e123e Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Thu, 21 Mar 2024 19:09:59 +0200 Subject: [PATCH 10/22] Update create_dataset.py --- scripts/ant/create_dataset.py | 1 - 1 file changed, 1 deletion(-) diff --git a/scripts/ant/create_dataset.py b/scripts/ant/create_dataset.py index 0ae64bf..a233863 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/ant/create_dataset.py @@ -62,7 +62,6 @@ def __call__(self, env, **kwargs): env.reset() dataset = collector_env.create_dataset( - # collector_env=collector_env, dataset_id=DATASET_NAME, algorithm_name="SB3/SAC", code_permalink="https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/blob/main/create_dataset.py", From 99ba394de7f930799a60a7f5c063387075e61a44 Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Thu, 21 Mar 2024 19:10:58 +0200 Subject: [PATCH 11/22] Create ant_v5_release.py --- scripts/ant/envs/ant_v5_release.py | 432 +++++++++++++++++++++++++++++ 1 file changed, 432 insertions(+) create mode 100644 scripts/ant/envs/ant_v5_release.py diff --git a/scripts/ant/envs/ant_v5_release.py b/scripts/ant/envs/ant_v5_release.py new file mode 100644 index 0000000..92b50b8 --- /dev/null +++ b/scripts/ant/envs/ant_v5_release.py @@ -0,0 +1,432 @@ +__credits__ = ["Kallinteris-Andreas"] + +from typing import Dict, Tuple, Union + +import numpy as np + +from gymnasium import utils +from gymnasium.envs.mujoco import MujocoEnv +from gymnasium.spaces import Box + + +DEFAULT_CAMERA_CONFIG = { + "distance": 4.0, +} + + +class AntEnv(MujocoEnv, utils.EzPickle): + r""" + ## Description + This environment is based on the one introduced by Schulman, Moritz, Levine, Jordan, and Abbeel in ["High-Dimensional Continuous Control Using Generalized Advantage Estimation"](https://arxiv.org/abs/1506.02438). + The ant is a 3D quadruped robot consisting of a torso (free rotational body) with four legs attached to it, where each leg has two body parts. + The goal is to coordinate the four legs to move in the forward (right) direction by applying torque to the eight hinges connecting the two body parts of each leg and the torso (nine body parts and eight hinges). + + Note: Although the robot is called "Ant", it is actually 75cm tall and weighs 910.88g. + + ## Action Space + ```{figure} action_space_figures/ant.png + :name: ant + ``` + + The action space is a `Box(-1, 1, (8,), float32)`. An action represents the torques applied at the hinge joints. + + | Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Type (Unit) | + | --- | ----------------------------------------------------------------- | ----------- | ----------- | -------------------------------- | ----- | ------------ | + | 0 | Torque applied on the rotor between the torso and back right hip | -1 | 1 | hip_4 (right_back_leg) | hinge | torque (N m) | + | 1 | Torque applied on the rotor between the back right two links | -1 | 1 | angle_4 (right_back_leg) | hinge | torque (N m) | + | 2 | Torque applied on the rotor between the torso and front left hip | -1 | 1 | hip_1 (front_left_leg) | hinge | torque (N m) | + | 3 | Torque applied on the rotor between the front left two links | -1 | 1 | angle_1 (front_left_leg) | hinge | torque (N m) | + | 4 | Torque applied on the rotor between the torso and front right hip | -1 | 1 | hip_2 (front_right_leg) | hinge | torque (N m) | + | 5 | Torque applied on the rotor between the front right two links | -1 | 1 | angle_2 (front_right_leg) | hinge | torque (N m) | + | 6 | Torque applied on the rotor between the torso and back left hip | -1 | 1 | hip_3 (back_leg) | hinge | torque (N m) | + | 7 | Torque applied on the rotor between the back left two links | -1 | 1 | angle_3 (back_leg) | hinge | torque (N m) | + + + ## Observation Space + The observation space consists of the following parts (in order): + + - *qpos (13 elements by default):* Position values of the robot's body parts. + - *qvel (14 elements):* The velocities of these individual body parts (their derivatives). + - *cfrc_ext (78 elements):* This is the center of mass based external forces on the body parts. + It has shape 13 * 6 (*nbody * 6*) and hence adds another 78 elements to the state space. + (external forces - force x, y, z and torque x, y, z) + + By default, the observation does not include the x- and y-coordinates of the torso. + These can be included by passing `exclude_current_positions_from_observation=False` during construction. + In this case, the observation space will be a `Box(-Inf, Inf, (107,), float64)`, where the first two observations are the x- and y-coordinates of the torso. + Regardless of whether `exclude_current_positions_from_observation` is set to `True` or `False`, the x- and y-coordinates are returned in `info` with the keys `"x_position"` and `"y_position"`, respectively. + + By default, however, the observation space is a `Box(-Inf, Inf, (105,), float64)`, where the position and velocity elements are as follows: + + | Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Type (Unit) | + |-----|--------------------------------------------------------------|--------|--------|----------------------------------------|-------|--------------------------| + | 0 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) | + | 1 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | + | 2 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | + | 3 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | + | 4 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | + | 5 | angle between torso and first link on front left | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) | + | 6 | angle between the two links on the front left | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) | + | 7 | angle between torso and first link on front right | -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) | + | 8 | angle between the two links on the front right | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) | + | 9 | angle between torso and first link on back left | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) | + | 10 | angle between the two links on the back left | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) | + | 11 | angle between torso and first link on back right | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) | + | 12 | angle between the two links on the back right | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) | + | 13 | x-coordinate velocity of the torso | -Inf | Inf | root | free | velocity (m/s) | + | 14 | y-coordinate velocity of the torso | -Inf | Inf | root | free | velocity (m/s) | + | 15 | z-coordinate velocity of the torso | -Inf | Inf | root | free | velocity (m/s) | + | 16 | x-coordinate angular velocity of the torso | -Inf | Inf | root | free | angular velocity (rad/s) | + | 17 | y-coordinate angular velocity of the torso | -Inf | Inf | root | free | angular velocity (rad/s) | + | 18 | z-coordinate angular velocity of the torso | -Inf | Inf | root | free | angular velocity (rad/s) | + | 19 | angular velocity of angle between torso and front left link | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) | + | 20 | angular velocity of the angle between front left links | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) | + | 21 | angular velocity of angle between torso and front right link | -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) | + | 22 | angular velocity of the angle between front right links | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) | + | 23 | angular velocity of angle between torso and back left link | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) | + | 24 | angular velocity of the angle between back left links | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) | + | 25 | angular velocity of angle between torso and back right link | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) | + | 26 | angular velocity of the angle between back right links | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) | + | excluded | x-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) | + | excluded | y-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) | + + The body parts are: + + | body part | id (for `v2`, `v3`, `v4)` | id (for `v5`) | + | ----------------------- | --- | --- | + | worldbody (note: all values are constant 0) | 0 |excluded| + | torso | 1 |0 | + | front_left_leg | 2 |1 | + | aux_1 (front left leg) | 3 |2 | + | ankle_1 (front left leg) | 4 |3 | + | front_right_leg | 5 |4 | + | aux_2 (front right leg) | 6 |5 | + | ankle_2 (front right leg) | 7 |6 | + | back_leg (back left leg) | 8 |7 | + | aux_3 (back left leg) | 9 |8 | + | ankle_3 (back left leg) | 10 |9 | + | right_back_leg | 11 |10 | + | aux_4 (back right leg) | 12 |11 | + | ankle_4 (back right leg) | 13 |12 | + + The (x,y,z) coordinates are translational DOFs, while the orientations are rotational DOFs expressed as quaternions. + One can read more about free joints in the [MuJoCo documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html). + + + **Note:** + When using Ant-v3 or earlier versions, problems have been reported when using a `mujoco-py` version > 2.0, resulting in contact forces always being 0. + Therefore, it is recommended to use a `mujoco-py` version < 2.0 when using the Ant environment if you want to report results with contact forces (if contact forces are not used in your experiments, you can use version > 2.0). + + + ## Rewards + The total reward is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost - contact_cost*. + + - *healthy_reward*: + Every timestep that the Ant is healthy (see definition in section "Episode End"), + it gets a reward of fixed value `healthy_reward` (default is $1$). + - *forward_reward*: + A reward for moving forward, + this reward would be positive if the Ant moves forward (in the positive $x$ direction / in the right direction). + $w_{forward} \times \frac{dx}{dt}$, where + $dx$ is the displacement of the `main_body` ($x_{after-action} - x_{before-action}$), + $dt$ is the time between actions, which depends on the `frame_skip` parameter (default is $5$), + and `frametime`, which is $0.01$ - so the default is $dt = 5 \times 0.01 = 0.05$, + $w_{forward}$ is the `forward_reward_weight` (default is $1$). + - *ctrl_cost*: + A negative reward to penalize the Ant for taking actions that are too large. + $w_{control} \times \|action\|_2^2$, + where $w_{control}$ is `ctrl_cost_weight` (default is $0.5$). + - *contact_cost*: + A negative reward to penalize the Ant if the external contact forces are too large. + $w_{contact} \times \|F_{contact}\|_2^2$, where + $w_{contact}$ is `contact_cost_weight` (default is $5\times10^{-4}$), + $F_{contact}$ are the external contact forces clipped by `contact_force_range` (see `cfrc_ext` section on Observation Space). + + `info` contains the individual reward terms. + + But if `use_contact_forces=False` on `v4` + The total reward returned is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost*. + + + ## Starting State + The initial position state is $[0.0, 0.0, 0.75, 1.0, 0.0, ... 0.0] + \mathcal{U}_{[-reset\_noise\_scale \times I_{15}, reset\_noise\_scale \times I_{15}]}$. + The initial velocity state is $\mathcal{N}(0_{14}, reset\_noise\_scale^2 \times I_{14})$. + + where $\mathcal{N}$ is the multivariate normal distribution and $\mathcal{U}$ is the multivariate uniform continuous distribution. + + Note that the z- and x-coordinates are non-zero so that the ant can immediately stand up and face forward (x-axis). + + + ## Episode End + ### Termination + If `terminate_when_unhealthy is True` (the default), the environment terminates when the Ant is unhealthy. + the Ant is unhealthy if any of the following happens: + + 1. Any of the state space values is no longer finite. + 2. The z-coordinate of the torso (the height) is **not** in the closed interval given by the `healthy_z_range` argument (default is $[0.2, 1.0]$). + + ### Truncation + The default duration of an episode is 1000 timesteps. + + + ## Arguments + Ant provides a range of parameters to modify the observation space, reward function, initial state, and termination condition. + These parameters can be applied during `gymnasium.make` in the following way: + + ```python + import gymnasium as gym + env = gym.make('Ant-v5', ctrl_cost_weight=0.5, ...) + ``` + + | Parameter | Type | Default |Description | + |--------------------------------------------|------------|--------------|-------------------------------| + |`xml_file` | **str** | `"ant.xml"` | Path to a MuJoCo model | + |`forward_reward_weight` | **float** | `1` | Weight for _forward_reward_ term (see `Rewards` section) | + |`ctrl_cost_weight` | **float** | `0.5` | Weight for _ctrl_cost_ term (see `Rewards` section) | + |`contact_cost_weight` | **float** | `5e-4` | Weight for _contact_cost_ term (see `Rewards` section) | + |`healthy_reward` | **float** | `1` | Weight for _healthy_reward_ term (see `Rewards` section) | + |`main_body` |**str\|int**| `1`("torso") | Name or ID of the body, whose displacement is used to calculate the *dx*/_forward_reward_ (useful for custom MuJoCo models) (see `Rewards` section) | + |`terminate_when_unhealthy` | **bool** | `True` | If `True`, issue a `terminated` signal is unhealthy (see `Episode End` section) | + |`healthy_z_range` | **tuple** | `(0.2, 1)` | The ant is considered healthy if the z-coordinate of the torso is in this range (see `Episode End` section) | + |`contact_force_range` | **tuple** | `(-1, 1)` | Contact forces are clipped to this range in the computation of *contact_cost* (see `Rewards` section) | + |`reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see `Starting State` section) | + |`exclude_current_positions_from_observation`| **bool** | `True` | Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies (see `Observation State` section) | + |`include_cfrc_ext_in_observation` | **bool** | `True` | Whether to include *cfrc_ext* elements in the observations (see `Observation State` section) | + |`use_contact_forces` (`v4` only) | **bool** | `False` | If `True`, it extends the observation space by adding contact forces (see `Observation Space` section) and includes contact_cost to the reward function (see `Rewards` section) | + + ## Version History + * v5: + - Minimum `mujoco` version is now 2.3.3. + - Added support for fully custom/third party `mujoco` models using the `xml_file` argument (previously only a few changes could be made to the existing models). + - Added `default_camera_config` argument, a dictionary for setting the `mj_camera` properties, mainly useful for custom environments. + - Added `env.observation_structure`, a dictionary for specifying the observation space compose (e.g. `qpos`, `qvel`), useful for building tooling and wrappers for the MuJoCo environments. + - Return a non-empty `info` with `reset()`, previously an empty dictionary was returned, the new keys are the same state information as `step()`. + - Added `frame_skip` argument, used to configure the `dt` (duration of `step()`), default varies by environment check environment documentation pages. + - Fixed bug: `healthy_reward` was given on every step (even if the Ant is unhealthy), now it is only given when the Ant is healthy. The `info["reward_survive"]` is updated with this change (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/526)). + - The reward function now always includes `contact_cost`, before it was only included if `use_contact_forces=True` (can be set to `0` with `contact_cost_weight=0`). + - Excluded the `cfrc_ext` of `worldbody` from the observation space, as it was always 0 and thus provided no useful information to the agent, resulting in slightly faster training (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/204)). + - Added the `main_body` argument, which specifies the body used to compute the forward reward (mainly useful for custom MuJoCo models). + - Added the `forward_reward_weight` argument, which defaults to `1` (effectively the same behavior as in `v4`). + - Added the `include_cfrc_ext_in_observation` argument, previously in `v4` the inclusion of `cfrc_ext` observations was controlled by `use_contact_forces` which defaulted to `False`, while `include_cfrc_ext_in_observation` defaults to `True`. + - Removed the `use_contact_forces` argument (note: its functionality has been replaced by `include_cfrc_ext_in_observation` and `contact_cost_weight`) (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/214)). + - Fixed `info["reward_ctrl"]` sometimes containing `contact_cost` instead of `ctrl_cost`. + - Fixed `info["x_position"]` & `info["y_position"]` & `info["distance_from_origin"]` giving `xpos` instead of `qpos` observations (`xpos` observations are behind 1 `mj_step()` more [here](https://github.com/deepmind/mujoco/issues/889#issuecomment-1568896388)) (related [GitHub issue #1](https://github.com/Farama-Foundation/Gymnasium/issues/521) & [GitHub issue #2](https://github.com/Farama-Foundation/Gymnasium/issues/539)). + - Removed `info["forward_reward"]` as it is equivalent to `info["reward_forward"]`. + * v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3, also removed contact forces from the default observation space (new variable `use_contact_forces=True` can restore them). + * v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). + * v2: All continuous control environments now use mujoco-py >= 1.50. + * v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments. + * v0: Initial versions release + """ + + metadata = { + "render_modes": [ + "human", + "rgb_array", + "depth_array", + ], + 'render_fps': 20 # added metadate to be compatible with the old version + } + + def __init__( + self, + xml_file: str = "ant.xml", + frame_skip: int = 5, + default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG, + forward_reward_weight: float = 1, + ctrl_cost_weight: float = 0.5, + contact_cost_weight: float = 5e-4, + healthy_reward: float = 1.0, + main_body: Union[int, str] = 1, + terminate_when_unhealthy: bool = True, + healthy_z_range: Tuple[float, float] = (0.2, 1.0), + contact_force_range: Tuple[float, float] = (-1.0, 1.0), + reset_noise_scale: float = 0.1, + exclude_current_positions_from_observation: bool = True, + include_cfrc_ext_in_observation: bool = True, + **kwargs, + ): + utils.EzPickle.__init__( + self, + xml_file, + frame_skip, + default_camera_config, + forward_reward_weight, + ctrl_cost_weight, + contact_cost_weight, + healthy_reward, + main_body, + terminate_when_unhealthy, + healthy_z_range, + contact_force_range, + reset_noise_scale, + exclude_current_positions_from_observation, + include_cfrc_ext_in_observation, + **kwargs, + ) + + self._forward_reward_weight = forward_reward_weight + self._ctrl_cost_weight = ctrl_cost_weight + self._contact_cost_weight = contact_cost_weight + + self._healthy_reward = healthy_reward + self._terminate_when_unhealthy = terminate_when_unhealthy + self._healthy_z_range = healthy_z_range + + self._contact_force_range = contact_force_range + + self._main_body = main_body + + self._reset_noise_scale = reset_noise_scale + + self._exclude_current_positions_from_observation = ( + exclude_current_positions_from_observation + ) + self._include_cfrc_ext_in_observation = include_cfrc_ext_in_observation + + MujocoEnv.__init__( + self, + xml_file, + frame_skip, + observation_space=None, # needs to be defined after + default_camera_config=default_camera_config, + **kwargs, + ) + + """ # disabled for compatabiltiy + self.metadata = { + "render_modes": [ + "human", + "rgb_array", + "depth_array", + ], + "render_fps": int(np.round(1.0 / self.dt)), + } + """ + + obs_size = self.data.qpos.size + self.data.qvel.size + obs_size -= 2 * exclude_current_positions_from_observation + obs_size += self.data.cfrc_ext[1:].size * include_cfrc_ext_in_observation + + self.observation_space = Box( + low=-np.inf, high=np.inf, shape=(obs_size,), dtype=np.float64 + ) + + self.observation_structure = { + "skipped_qpos": 2 * exclude_current_positions_from_observation, + "qpos": self.data.qpos.size + - 2 * exclude_current_positions_from_observation, + "qvel": self.data.qvel.size, + "cfrc_ext": self.data.cfrc_ext[1:].size * include_cfrc_ext_in_observation, + } + + @property + def healthy_reward(self): + return self.is_healthy * self._healthy_reward + + def control_cost(self, action): + control_cost = self._ctrl_cost_weight * np.sum(np.square(action)) + return control_cost + + @property + def contact_forces(self): + raw_contact_forces = self.data.cfrc_ext + min_value, max_value = self._contact_force_range + contact_forces = np.clip(raw_contact_forces, min_value, max_value) + return contact_forces + + @property + def contact_cost(self): + contact_cost = self._contact_cost_weight * np.sum( + np.square(self.contact_forces) + ) + return contact_cost + + @property + def is_healthy(self): + state = self.state_vector() + min_z, max_z = self._healthy_z_range + is_healthy = np.isfinite(state).all() and min_z <= state[2] <= max_z + return is_healthy + + def step(self, action): + xy_position_before = self.data.body(self._main_body).xpos[:2].copy() + self.do_simulation(action, self.frame_skip) + xy_position_after = self.data.body(self._main_body).xpos[:2].copy() + + xy_velocity = (xy_position_after - xy_position_before) / self.dt + x_velocity, y_velocity = xy_velocity + + observation = self._get_obs() + reward, reward_info = self._get_rew(x_velocity, action) + terminated = (not self.is_healthy) and self._terminate_when_unhealthy + info = { + "x_position": self.data.qpos[0], + "y_position": self.data.qpos[1], + "distance_from_origin": np.linalg.norm(self.data.qpos[0:2], ord=2), + "x_velocity": x_velocity, + "y_velocity": y_velocity, + **reward_info, + } + + if self.render_mode == "human": + self.render() + # truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make` + return observation, reward, terminated, False, info + + def _get_rew(self, x_velocity: float, action): + forward_reward = x_velocity * self._forward_reward_weight + healthy_reward = self.healthy_reward + rewards = forward_reward + healthy_reward + + ctrl_cost = self.control_cost(action) + contact_cost = self.contact_cost + costs = ctrl_cost + contact_cost + + reward = rewards - costs + + reward_info = { + "reward_forward": forward_reward, + "reward_ctrl": -ctrl_cost, + "reward_contact": -contact_cost, + "reward_survive": healthy_reward, + } + + return reward, reward_info + + def _get_obs(self): + position = self.data.qpos.flatten() + velocity = self.data.qvel.flatten() + + if self._exclude_current_positions_from_observation: + position = position[2:] + + if self._include_cfrc_ext_in_observation: + contact_force = self.contact_forces[1:].flatten() + return np.concatenate((position, velocity, contact_force)) + else: + return np.concatenate((position, velocity)) + + def reset_model(self): + noise_low = -self._reset_noise_scale + noise_high = self._reset_noise_scale + + qpos = self.init_qpos + self.np_random.uniform( + low=noise_low, high=noise_high, size=self.model.nq + ) + qvel = ( + self.init_qvel + + self._reset_noise_scale * self.np_random.standard_normal(self.model.nv) + ) + self.set_state(qpos, qvel) + + observation = self._get_obs() + + return observation + + def _get_reset_info(self): + return { + "x_position": self.data.qpos[0], + "y_position": self.data.qpos[1], + "distance_from_origin": np.linalg.norm(self.data.qpos[0:2], ord=2), + } From 10b94d498b4a3b91d2516cd34762c33ed22b68cd Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Sun, 13 Oct 2024 22:49:06 +0300 Subject: [PATCH 12/22] update --- .../Ant-expert-finetuned-v0}/best_model.zip | Bin .../create_dataset.py | 31 +- .../Ant-expert-finetuned-v0/requirements.txt | 107 +++++ scripts/ant/envs/ant_v5_release.py | 432 ------------------ 4 files changed, 126 insertions(+), 444 deletions(-) rename scripts/{ant => MuJoCo/Ant-expert-finetuned-v0}/best_model.zip (100%) rename scripts/{ant => MuJoCo/Ant-expert-finetuned-v0}/create_dataset.py (65%) create mode 100644 scripts/MuJoCo/Ant-expert-finetuned-v0/requirements.txt delete mode 100644 scripts/ant/envs/ant_v5_release.py diff --git a/scripts/ant/best_model.zip b/scripts/MuJoCo/Ant-expert-finetuned-v0/best_model.zip similarity index 100% rename from scripts/ant/best_model.zip rename to scripts/MuJoCo/Ant-expert-finetuned-v0/best_model.zip diff --git a/scripts/ant/create_dataset.py b/scripts/MuJoCo/Ant-expert-finetuned-v0/create_dataset.py similarity index 65% rename from scripts/ant/create_dataset.py rename to scripts/MuJoCo/Ant-expert-finetuned-v0/create_dataset.py index a233863..cee7c2e 100644 --- a/scripts/ant/create_dataset.py +++ b/scripts/MuJoCo/Ant-expert-finetuned-v0/create_dataset.py @@ -3,26 +3,25 @@ import gymnasium as gym import minari import numpy as np -import stable_baselines3 from minari import DataCollector, StepDataCallback -from stable_baselines3 import A2C, PPO, SAC, TD3 - -from envs import ant_v5_release -from gymnasium.wrappers import TransformReward, PassiveEnvChecker, OrderEnforcing, TimeLimit - +from stable_baselines3 import SAC SEED = 12345 NUM_STEPS = int(2e6) + class AddExcludedObservationElements(StepDataCallback): """Add Excluded observation elements like cfrc_ext to the observation space.""" + def __call__(self, env, **kwargs): step_data = super().__call__(env, **kwargs) # if getattr(env, "_include_cinert_in_observation", None) is False: # if getattr(env, "_include_cvel_in_observation ", None) is False: # if getattr(env, "_include_qfrc_actuator_in_observation ", None) is False: if env.unwrapped._include_cfrc_ext_in_observation is False: - step_data["observations"] = np.concatenate([step_data["observations"], env.unwrapped.contact_forces[1:].flat.copy()]) + step_data["observation"] = np.concatenate( + [step_data["observation"], env.unwrapped.contact_forces[1:].flat.copy()] + ) return step_data @@ -34,17 +33,22 @@ def __call__(self, env, **kwargs): assert DATASET_NAME not in minari.list_local_datasets() # Create Environment -#env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1000) -env = TimeLimit(OrderEnforcing(PassiveEnvChecker(ant_v5_release.AntEnv(include_cfrc_ext_in_observation=False))), max_episode_steps=1000) +env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1000) # add callback to add cfrc_ext to the obs space -collector_env = DataCollector(env, step_data_callback=AddExcludedObservationElements, record_infos=False, action_space=env.action_space, observation_space=gym.spaces.Box(-np.inf, np.inf, (105,), np.float64)) +collector_env = DataCollector( + env, + step_data_callback=AddExcludedObservationElements, + record_infos=False, + action_space=env.action_space, + observation_space=gym.spaces.Box(-np.inf, np.inf, (105,), np.float64), +) # we do not observe `cfrc_ext` with the agent, but we keep it in the dataset observations obs, _ = collector_env.reset(seed=SEED) # load policy model # model is from: https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/tree/main/results/ant_v5_without_ctn_SAC/run_9 model = SAC.load( - path="./results/ant_v5_without_ctn_SAC/run_9/best_model.zip", + path="./best_model.zip", env=env, device="cpu", ) @@ -63,9 +67,12 @@ def __call__(self, env, **kwargs): dataset = collector_env.create_dataset( dataset_id=DATASET_NAME, + eval_env="Ant-v5", algorithm_name="SB3/SAC", - code_permalink="https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/blob/main/create_dataset.py", + code_permalink="https://github.com/Farama-Foundation/minari-dataset-generation-scripts/tree/main/scripts/MuJoCo/Ant/expert_dataset.py", author="Kallinteris Andreas", author_email="kallinteris@protonmail.com", + description="Ant expert fine tuned policy, it was trained with `include_cfrc_ext_in_observation=False`, model training at https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation .", + requirements=["mujoco==3.2.3",], # env_spec ) diff --git a/scripts/MuJoCo/Ant-expert-finetuned-v0/requirements.txt b/scripts/MuJoCo/Ant-expert-finetuned-v0/requirements.txt new file mode 100644 index 0000000..66fa5af --- /dev/null +++ b/scripts/MuJoCo/Ant-expert-finetuned-v0/requirements.txt @@ -0,0 +1,107 @@ +absl-py==2.1.0 +ale-py==0.10.1 +box2d-py==2.3.5 +cachetools==5.5.0 +certifi==2024.8.30 +cffi==1.17.1 +charset-normalizer==3.4.0 +chex==0.1.87 +click==8.1.7 +cloudpickle==3.1.0 +contourpy==1.3.0 +cycler==0.12.1 +Cython==0.29.37 +decorator==4.4.2 +etils==1.9.4 +Farama-Notifications==0.0.4 +fasteners==0.19 +filelock==3.16.1 +flax==0.9.0 +fonttools==4.54.1 +fsspec==2024.9.0 +glfw==2.7.0 +google-api-core==2.21.0 +google-auth==2.35.0 +google-cloud-core==2.4.1 +google-cloud-storage==2.18.2 +google-crc32c==1.6.0 +google-resumable-media==2.7.2 +googleapis-common-protos==1.65.0 +gymnasium==1.0.0 +h5py==3.12.1 +humanize==4.11.0 +idna==3.10 +imageio==2.35.1 +imageio-ffmpeg==0.5.1 +importlib_resources==6.4.5 +jax==0.4.34 +jaxlib==0.4.34 +Jinja2==3.1.4 +kiwisolver==1.4.7 +markdown-it-py==3.0.0 +MarkupSafe==3.0.1 +matplotlib==3.9.2 +mdurl==0.1.2 +minari==0.5.1 +ml_dtypes==0.5.0 +moviepy==1.0.3 +mpmath==1.3.0 +msgpack==1.1.0 +mujoco==3.2.3 +mujoco-py==2.1.2.14 +nest-asyncio==1.6.0 +networkx==3.4.1 +numpy==1.26.4 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-nccl-cu12==2.20.5 +nvidia-nvjitlink-cu12==12.6.77 +nvidia-nvtx-cu12==12.1.105 +opencv-python==4.10.0.84 +opt_einsum==3.4.0 +optax==0.2.3 +orbax-checkpoint==0.7.0 +packaging==24.1 +pandas==2.2.3 +pillow==10.4.0 +proglog==0.1.10 +proto-plus==1.24.0 +protobuf==5.28.2 +pyarrow==17.0.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycparser==2.22 +pygame==2.6.1 +Pygments==2.18.0 +PyOpenGL==3.1.7 +pyparsing==3.1.4 +python-dateutil==2.9.0.post0 +pytz==2024.2 +PyYAML==6.0.2 +requests==2.32.3 +rich==13.9.2 +rsa==4.9 +scipy==1.14.1 +setuptools==75.1.0 +shellingham==1.5.4 +six==1.16.0 +stable-baselines3=git+https://github.com/pseudo-rnd-thoughts/stable-baselines3.git@2f6246033cb9111ad0d0df9d9bd98555957e08b5#egg=stable_baselines3 +swig==4.2.1 +sympy==1.13.3 +tensorstore==0.1.66 +toolz==1.0.0 +torch==2.4.1 +tqdm==4.66.5 +triton==3.0.0 +typer==0.12.5 +typing_extensions==4.12.2 +tzdata==2024.2 +urllib3==2.2.3 +zipp==3.20.2 diff --git a/scripts/ant/envs/ant_v5_release.py b/scripts/ant/envs/ant_v5_release.py deleted file mode 100644 index 92b50b8..0000000 --- a/scripts/ant/envs/ant_v5_release.py +++ /dev/null @@ -1,432 +0,0 @@ -__credits__ = ["Kallinteris-Andreas"] - -from typing import Dict, Tuple, Union - -import numpy as np - -from gymnasium import utils -from gymnasium.envs.mujoco import MujocoEnv -from gymnasium.spaces import Box - - -DEFAULT_CAMERA_CONFIG = { - "distance": 4.0, -} - - -class AntEnv(MujocoEnv, utils.EzPickle): - r""" - ## Description - This environment is based on the one introduced by Schulman, Moritz, Levine, Jordan, and Abbeel in ["High-Dimensional Continuous Control Using Generalized Advantage Estimation"](https://arxiv.org/abs/1506.02438). - The ant is a 3D quadruped robot consisting of a torso (free rotational body) with four legs attached to it, where each leg has two body parts. - The goal is to coordinate the four legs to move in the forward (right) direction by applying torque to the eight hinges connecting the two body parts of each leg and the torso (nine body parts and eight hinges). - - Note: Although the robot is called "Ant", it is actually 75cm tall and weighs 910.88g. - - ## Action Space - ```{figure} action_space_figures/ant.png - :name: ant - ``` - - The action space is a `Box(-1, 1, (8,), float32)`. An action represents the torques applied at the hinge joints. - - | Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Type (Unit) | - | --- | ----------------------------------------------------------------- | ----------- | ----------- | -------------------------------- | ----- | ------------ | - | 0 | Torque applied on the rotor between the torso and back right hip | -1 | 1 | hip_4 (right_back_leg) | hinge | torque (N m) | - | 1 | Torque applied on the rotor between the back right two links | -1 | 1 | angle_4 (right_back_leg) | hinge | torque (N m) | - | 2 | Torque applied on the rotor between the torso and front left hip | -1 | 1 | hip_1 (front_left_leg) | hinge | torque (N m) | - | 3 | Torque applied on the rotor between the front left two links | -1 | 1 | angle_1 (front_left_leg) | hinge | torque (N m) | - | 4 | Torque applied on the rotor between the torso and front right hip | -1 | 1 | hip_2 (front_right_leg) | hinge | torque (N m) | - | 5 | Torque applied on the rotor between the front right two links | -1 | 1 | angle_2 (front_right_leg) | hinge | torque (N m) | - | 6 | Torque applied on the rotor between the torso and back left hip | -1 | 1 | hip_3 (back_leg) | hinge | torque (N m) | - | 7 | Torque applied on the rotor between the back left two links | -1 | 1 | angle_3 (back_leg) | hinge | torque (N m) | - - - ## Observation Space - The observation space consists of the following parts (in order): - - - *qpos (13 elements by default):* Position values of the robot's body parts. - - *qvel (14 elements):* The velocities of these individual body parts (their derivatives). - - *cfrc_ext (78 elements):* This is the center of mass based external forces on the body parts. - It has shape 13 * 6 (*nbody * 6*) and hence adds another 78 elements to the state space. - (external forces - force x, y, z and torque x, y, z) - - By default, the observation does not include the x- and y-coordinates of the torso. - These can be included by passing `exclude_current_positions_from_observation=False` during construction. - In this case, the observation space will be a `Box(-Inf, Inf, (107,), float64)`, where the first two observations are the x- and y-coordinates of the torso. - Regardless of whether `exclude_current_positions_from_observation` is set to `True` or `False`, the x- and y-coordinates are returned in `info` with the keys `"x_position"` and `"y_position"`, respectively. - - By default, however, the observation space is a `Box(-Inf, Inf, (105,), float64)`, where the position and velocity elements are as follows: - - | Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Type (Unit) | - |-----|--------------------------------------------------------------|--------|--------|----------------------------------------|-------|--------------------------| - | 0 | z-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) | - | 1 | x-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | - | 2 | y-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | - | 3 | z-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | - | 4 | w-orientation of the torso (centre) | -Inf | Inf | root | free | angle (rad) | - | 5 | angle between torso and first link on front left | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) | - | 6 | angle between the two links on the front left | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) | - | 7 | angle between torso and first link on front right | -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) | - | 8 | angle between the two links on the front right | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) | - | 9 | angle between torso and first link on back left | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) | - | 10 | angle between the two links on the back left | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) | - | 11 | angle between torso and first link on back right | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) | - | 12 | angle between the two links on the back right | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) | - | 13 | x-coordinate velocity of the torso | -Inf | Inf | root | free | velocity (m/s) | - | 14 | y-coordinate velocity of the torso | -Inf | Inf | root | free | velocity (m/s) | - | 15 | z-coordinate velocity of the torso | -Inf | Inf | root | free | velocity (m/s) | - | 16 | x-coordinate angular velocity of the torso | -Inf | Inf | root | free | angular velocity (rad/s) | - | 17 | y-coordinate angular velocity of the torso | -Inf | Inf | root | free | angular velocity (rad/s) | - | 18 | z-coordinate angular velocity of the torso | -Inf | Inf | root | free | angular velocity (rad/s) | - | 19 | angular velocity of angle between torso and front left link | -Inf | Inf | hip_1 (front_left_leg) | hinge | angle (rad) | - | 20 | angular velocity of the angle between front left links | -Inf | Inf | ankle_1 (front_left_leg) | hinge | angle (rad) | - | 21 | angular velocity of angle between torso and front right link | -Inf | Inf | hip_2 (front_right_leg) | hinge | angle (rad) | - | 22 | angular velocity of the angle between front right links | -Inf | Inf | ankle_2 (front_right_leg) | hinge | angle (rad) | - | 23 | angular velocity of angle between torso and back left link | -Inf | Inf | hip_3 (back_leg) | hinge | angle (rad) | - | 24 | angular velocity of the angle between back left links | -Inf | Inf | ankle_3 (back_leg) | hinge | angle (rad) | - | 25 | angular velocity of angle between torso and back right link | -Inf | Inf | hip_4 (right_back_leg) | hinge | angle (rad) | - | 26 | angular velocity of the angle between back right links | -Inf | Inf | ankle_4 (right_back_leg) | hinge | angle (rad) | - | excluded | x-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) | - | excluded | y-coordinate of the torso (centre) | -Inf | Inf | root | free | position (m) | - - The body parts are: - - | body part | id (for `v2`, `v3`, `v4)` | id (for `v5`) | - | ----------------------- | --- | --- | - | worldbody (note: all values are constant 0) | 0 |excluded| - | torso | 1 |0 | - | front_left_leg | 2 |1 | - | aux_1 (front left leg) | 3 |2 | - | ankle_1 (front left leg) | 4 |3 | - | front_right_leg | 5 |4 | - | aux_2 (front right leg) | 6 |5 | - | ankle_2 (front right leg) | 7 |6 | - | back_leg (back left leg) | 8 |7 | - | aux_3 (back left leg) | 9 |8 | - | ankle_3 (back left leg) | 10 |9 | - | right_back_leg | 11 |10 | - | aux_4 (back right leg) | 12 |11 | - | ankle_4 (back right leg) | 13 |12 | - - The (x,y,z) coordinates are translational DOFs, while the orientations are rotational DOFs expressed as quaternions. - One can read more about free joints in the [MuJoCo documentation](https://mujoco.readthedocs.io/en/latest/XMLreference.html). - - - **Note:** - When using Ant-v3 or earlier versions, problems have been reported when using a `mujoco-py` version > 2.0, resulting in contact forces always being 0. - Therefore, it is recommended to use a `mujoco-py` version < 2.0 when using the Ant environment if you want to report results with contact forces (if contact forces are not used in your experiments, you can use version > 2.0). - - - ## Rewards - The total reward is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost - contact_cost*. - - - *healthy_reward*: - Every timestep that the Ant is healthy (see definition in section "Episode End"), - it gets a reward of fixed value `healthy_reward` (default is $1$). - - *forward_reward*: - A reward for moving forward, - this reward would be positive if the Ant moves forward (in the positive $x$ direction / in the right direction). - $w_{forward} \times \frac{dx}{dt}$, where - $dx$ is the displacement of the `main_body` ($x_{after-action} - x_{before-action}$), - $dt$ is the time between actions, which depends on the `frame_skip` parameter (default is $5$), - and `frametime`, which is $0.01$ - so the default is $dt = 5 \times 0.01 = 0.05$, - $w_{forward}$ is the `forward_reward_weight` (default is $1$). - - *ctrl_cost*: - A negative reward to penalize the Ant for taking actions that are too large. - $w_{control} \times \|action\|_2^2$, - where $w_{control}$ is `ctrl_cost_weight` (default is $0.5$). - - *contact_cost*: - A negative reward to penalize the Ant if the external contact forces are too large. - $w_{contact} \times \|F_{contact}\|_2^2$, where - $w_{contact}$ is `contact_cost_weight` (default is $5\times10^{-4}$), - $F_{contact}$ are the external contact forces clipped by `contact_force_range` (see `cfrc_ext` section on Observation Space). - - `info` contains the individual reward terms. - - But if `use_contact_forces=False` on `v4` - The total reward returned is ***reward*** *=* *healthy_reward + forward_reward - ctrl_cost*. - - - ## Starting State - The initial position state is $[0.0, 0.0, 0.75, 1.0, 0.0, ... 0.0] + \mathcal{U}_{[-reset\_noise\_scale \times I_{15}, reset\_noise\_scale \times I_{15}]}$. - The initial velocity state is $\mathcal{N}(0_{14}, reset\_noise\_scale^2 \times I_{14})$. - - where $\mathcal{N}$ is the multivariate normal distribution and $\mathcal{U}$ is the multivariate uniform continuous distribution. - - Note that the z- and x-coordinates are non-zero so that the ant can immediately stand up and face forward (x-axis). - - - ## Episode End - ### Termination - If `terminate_when_unhealthy is True` (the default), the environment terminates when the Ant is unhealthy. - the Ant is unhealthy if any of the following happens: - - 1. Any of the state space values is no longer finite. - 2. The z-coordinate of the torso (the height) is **not** in the closed interval given by the `healthy_z_range` argument (default is $[0.2, 1.0]$). - - ### Truncation - The default duration of an episode is 1000 timesteps. - - - ## Arguments - Ant provides a range of parameters to modify the observation space, reward function, initial state, and termination condition. - These parameters can be applied during `gymnasium.make` in the following way: - - ```python - import gymnasium as gym - env = gym.make('Ant-v5', ctrl_cost_weight=0.5, ...) - ``` - - | Parameter | Type | Default |Description | - |--------------------------------------------|------------|--------------|-------------------------------| - |`xml_file` | **str** | `"ant.xml"` | Path to a MuJoCo model | - |`forward_reward_weight` | **float** | `1` | Weight for _forward_reward_ term (see `Rewards` section) | - |`ctrl_cost_weight` | **float** | `0.5` | Weight for _ctrl_cost_ term (see `Rewards` section) | - |`contact_cost_weight` | **float** | `5e-4` | Weight for _contact_cost_ term (see `Rewards` section) | - |`healthy_reward` | **float** | `1` | Weight for _healthy_reward_ term (see `Rewards` section) | - |`main_body` |**str\|int**| `1`("torso") | Name or ID of the body, whose displacement is used to calculate the *dx*/_forward_reward_ (useful for custom MuJoCo models) (see `Rewards` section) | - |`terminate_when_unhealthy` | **bool** | `True` | If `True`, issue a `terminated` signal is unhealthy (see `Episode End` section) | - |`healthy_z_range` | **tuple** | `(0.2, 1)` | The ant is considered healthy if the z-coordinate of the torso is in this range (see `Episode End` section) | - |`contact_force_range` | **tuple** | `(-1, 1)` | Contact forces are clipped to this range in the computation of *contact_cost* (see `Rewards` section) | - |`reset_noise_scale` | **float** | `0.1` | Scale of random perturbations of initial position and velocity (see `Starting State` section) | - |`exclude_current_positions_from_observation`| **bool** | `True` | Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies (see `Observation State` section) | - |`include_cfrc_ext_in_observation` | **bool** | `True` | Whether to include *cfrc_ext* elements in the observations (see `Observation State` section) | - |`use_contact_forces` (`v4` only) | **bool** | `False` | If `True`, it extends the observation space by adding contact forces (see `Observation Space` section) and includes contact_cost to the reward function (see `Rewards` section) | - - ## Version History - * v5: - - Minimum `mujoco` version is now 2.3.3. - - Added support for fully custom/third party `mujoco` models using the `xml_file` argument (previously only a few changes could be made to the existing models). - - Added `default_camera_config` argument, a dictionary for setting the `mj_camera` properties, mainly useful for custom environments. - - Added `env.observation_structure`, a dictionary for specifying the observation space compose (e.g. `qpos`, `qvel`), useful for building tooling and wrappers for the MuJoCo environments. - - Return a non-empty `info` with `reset()`, previously an empty dictionary was returned, the new keys are the same state information as `step()`. - - Added `frame_skip` argument, used to configure the `dt` (duration of `step()`), default varies by environment check environment documentation pages. - - Fixed bug: `healthy_reward` was given on every step (even if the Ant is unhealthy), now it is only given when the Ant is healthy. The `info["reward_survive"]` is updated with this change (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/526)). - - The reward function now always includes `contact_cost`, before it was only included if `use_contact_forces=True` (can be set to `0` with `contact_cost_weight=0`). - - Excluded the `cfrc_ext` of `worldbody` from the observation space, as it was always 0 and thus provided no useful information to the agent, resulting in slightly faster training (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/204)). - - Added the `main_body` argument, which specifies the body used to compute the forward reward (mainly useful for custom MuJoCo models). - - Added the `forward_reward_weight` argument, which defaults to `1` (effectively the same behavior as in `v4`). - - Added the `include_cfrc_ext_in_observation` argument, previously in `v4` the inclusion of `cfrc_ext` observations was controlled by `use_contact_forces` which defaulted to `False`, while `include_cfrc_ext_in_observation` defaults to `True`. - - Removed the `use_contact_forces` argument (note: its functionality has been replaced by `include_cfrc_ext_in_observation` and `contact_cost_weight`) (related [GitHub issue](https://github.com/Farama-Foundation/Gymnasium/issues/214)). - - Fixed `info["reward_ctrl"]` sometimes containing `contact_cost` instead of `ctrl_cost`. - - Fixed `info["x_position"]` & `info["y_position"]` & `info["distance_from_origin"]` giving `xpos` instead of `qpos` observations (`xpos` observations are behind 1 `mj_step()` more [here](https://github.com/deepmind/mujoco/issues/889#issuecomment-1568896388)) (related [GitHub issue #1](https://github.com/Farama-Foundation/Gymnasium/issues/521) & [GitHub issue #2](https://github.com/Farama-Foundation/Gymnasium/issues/539)). - - Removed `info["forward_reward"]` as it is equivalent to `info["reward_forward"]`. - * v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3, also removed contact forces from the default observation space (new variable `use_contact_forces=True` can restore them). - * v3: Support for `gymnasium.make` kwargs such as `xml_file`, `ctrl_cost_weight`, `reset_noise_scale`, etc. rgb rendering comes from tracking camera (so agent does not run away from screen). - * v2: All continuous control environments now use mujoco-py >= 1.50. - * v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments. - * v0: Initial versions release - """ - - metadata = { - "render_modes": [ - "human", - "rgb_array", - "depth_array", - ], - 'render_fps': 20 # added metadate to be compatible with the old version - } - - def __init__( - self, - xml_file: str = "ant.xml", - frame_skip: int = 5, - default_camera_config: Dict[str, Union[float, int]] = DEFAULT_CAMERA_CONFIG, - forward_reward_weight: float = 1, - ctrl_cost_weight: float = 0.5, - contact_cost_weight: float = 5e-4, - healthy_reward: float = 1.0, - main_body: Union[int, str] = 1, - terminate_when_unhealthy: bool = True, - healthy_z_range: Tuple[float, float] = (0.2, 1.0), - contact_force_range: Tuple[float, float] = (-1.0, 1.0), - reset_noise_scale: float = 0.1, - exclude_current_positions_from_observation: bool = True, - include_cfrc_ext_in_observation: bool = True, - **kwargs, - ): - utils.EzPickle.__init__( - self, - xml_file, - frame_skip, - default_camera_config, - forward_reward_weight, - ctrl_cost_weight, - contact_cost_weight, - healthy_reward, - main_body, - terminate_when_unhealthy, - healthy_z_range, - contact_force_range, - reset_noise_scale, - exclude_current_positions_from_observation, - include_cfrc_ext_in_observation, - **kwargs, - ) - - self._forward_reward_weight = forward_reward_weight - self._ctrl_cost_weight = ctrl_cost_weight - self._contact_cost_weight = contact_cost_weight - - self._healthy_reward = healthy_reward - self._terminate_when_unhealthy = terminate_when_unhealthy - self._healthy_z_range = healthy_z_range - - self._contact_force_range = contact_force_range - - self._main_body = main_body - - self._reset_noise_scale = reset_noise_scale - - self._exclude_current_positions_from_observation = ( - exclude_current_positions_from_observation - ) - self._include_cfrc_ext_in_observation = include_cfrc_ext_in_observation - - MujocoEnv.__init__( - self, - xml_file, - frame_skip, - observation_space=None, # needs to be defined after - default_camera_config=default_camera_config, - **kwargs, - ) - - """ # disabled for compatabiltiy - self.metadata = { - "render_modes": [ - "human", - "rgb_array", - "depth_array", - ], - "render_fps": int(np.round(1.0 / self.dt)), - } - """ - - obs_size = self.data.qpos.size + self.data.qvel.size - obs_size -= 2 * exclude_current_positions_from_observation - obs_size += self.data.cfrc_ext[1:].size * include_cfrc_ext_in_observation - - self.observation_space = Box( - low=-np.inf, high=np.inf, shape=(obs_size,), dtype=np.float64 - ) - - self.observation_structure = { - "skipped_qpos": 2 * exclude_current_positions_from_observation, - "qpos": self.data.qpos.size - - 2 * exclude_current_positions_from_observation, - "qvel": self.data.qvel.size, - "cfrc_ext": self.data.cfrc_ext[1:].size * include_cfrc_ext_in_observation, - } - - @property - def healthy_reward(self): - return self.is_healthy * self._healthy_reward - - def control_cost(self, action): - control_cost = self._ctrl_cost_weight * np.sum(np.square(action)) - return control_cost - - @property - def contact_forces(self): - raw_contact_forces = self.data.cfrc_ext - min_value, max_value = self._contact_force_range - contact_forces = np.clip(raw_contact_forces, min_value, max_value) - return contact_forces - - @property - def contact_cost(self): - contact_cost = self._contact_cost_weight * np.sum( - np.square(self.contact_forces) - ) - return contact_cost - - @property - def is_healthy(self): - state = self.state_vector() - min_z, max_z = self._healthy_z_range - is_healthy = np.isfinite(state).all() and min_z <= state[2] <= max_z - return is_healthy - - def step(self, action): - xy_position_before = self.data.body(self._main_body).xpos[:2].copy() - self.do_simulation(action, self.frame_skip) - xy_position_after = self.data.body(self._main_body).xpos[:2].copy() - - xy_velocity = (xy_position_after - xy_position_before) / self.dt - x_velocity, y_velocity = xy_velocity - - observation = self._get_obs() - reward, reward_info = self._get_rew(x_velocity, action) - terminated = (not self.is_healthy) and self._terminate_when_unhealthy - info = { - "x_position": self.data.qpos[0], - "y_position": self.data.qpos[1], - "distance_from_origin": np.linalg.norm(self.data.qpos[0:2], ord=2), - "x_velocity": x_velocity, - "y_velocity": y_velocity, - **reward_info, - } - - if self.render_mode == "human": - self.render() - # truncation=False as the time limit is handled by the `TimeLimit` wrapper added during `make` - return observation, reward, terminated, False, info - - def _get_rew(self, x_velocity: float, action): - forward_reward = x_velocity * self._forward_reward_weight - healthy_reward = self.healthy_reward - rewards = forward_reward + healthy_reward - - ctrl_cost = self.control_cost(action) - contact_cost = self.contact_cost - costs = ctrl_cost + contact_cost - - reward = rewards - costs - - reward_info = { - "reward_forward": forward_reward, - "reward_ctrl": -ctrl_cost, - "reward_contact": -contact_cost, - "reward_survive": healthy_reward, - } - - return reward, reward_info - - def _get_obs(self): - position = self.data.qpos.flatten() - velocity = self.data.qvel.flatten() - - if self._exclude_current_positions_from_observation: - position = position[2:] - - if self._include_cfrc_ext_in_observation: - contact_force = self.contact_forces[1:].flatten() - return np.concatenate((position, velocity, contact_force)) - else: - return np.concatenate((position, velocity)) - - def reset_model(self): - noise_low = -self._reset_noise_scale - noise_high = self._reset_noise_scale - - qpos = self.init_qpos + self.np_random.uniform( - low=noise_low, high=noise_high, size=self.model.nq - ) - qvel = ( - self.init_qvel - + self._reset_noise_scale * self.np_random.standard_normal(self.model.nv) - ) - self.set_state(qpos, qvel) - - observation = self._get_obs() - - return observation - - def _get_reset_info(self): - return { - "x_position": self.data.qpos[0], - "y_position": self.data.qpos[1], - "distance_from_origin": np.linalg.norm(self.data.qpos[0:2], ord=2), - } From a46cd3aa70a3a681b46acc0dba25c5a45e6e2505 Mon Sep 17 00:00:00 2001 From: Omar Younis Date: Sun, 13 Oct 2024 23:20:33 +0200 Subject: [PATCH 13/22] propose changes --- .../Ant-expert-finetuned-v0/create_dataset.py | 78 ------------------ .../ant}/best_model.zip | Bin scripts/mujoco/ant/create_dataset.py | 46 +++++++++++ .../ant}/requirements.txt | 2 +- 4 files changed, 47 insertions(+), 79 deletions(-) delete mode 100644 scripts/MuJoCo/Ant-expert-finetuned-v0/create_dataset.py rename scripts/{MuJoCo/Ant-expert-finetuned-v0 => mujoco/ant}/best_model.zip (100%) create mode 100644 scripts/mujoco/ant/create_dataset.py rename scripts/{MuJoCo/Ant-expert-finetuned-v0 => mujoco/ant}/requirements.txt (96%) diff --git a/scripts/MuJoCo/Ant-expert-finetuned-v0/create_dataset.py b/scripts/MuJoCo/Ant-expert-finetuned-v0/create_dataset.py deleted file mode 100644 index cee7c2e..0000000 --- a/scripts/MuJoCo/Ant-expert-finetuned-v0/create_dataset.py +++ /dev/null @@ -1,78 +0,0 @@ -__credits__ = ["Kallinteris Andreas"] - -import gymnasium as gym -import minari -import numpy as np -from minari import DataCollector, StepDataCallback -from stable_baselines3 import SAC - -SEED = 12345 -NUM_STEPS = int(2e6) - - -class AddExcludedObservationElements(StepDataCallback): - """Add Excluded observation elements like cfrc_ext to the observation space.""" - - def __call__(self, env, **kwargs): - step_data = super().__call__(env, **kwargs) - # if getattr(env, "_include_cinert_in_observation", None) is False: - # if getattr(env, "_include_cvel_in_observation ", None) is False: - # if getattr(env, "_include_qfrc_actuator_in_observation ", None) is False: - if env.unwrapped._include_cfrc_ext_in_observation is False: - step_data["observation"] = np.concatenate( - [step_data["observation"], env.unwrapped.contact_forces[1:].flat.copy()] - ) - - return step_data - - -DATASET_NAME = "ant-v5-expert-tuned-v0" -dataset = None - -# Check if dataset already exist -assert DATASET_NAME not in minari.list_local_datasets() - -# Create Environment -env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1000) -# add callback to add cfrc_ext to the obs space -collector_env = DataCollector( - env, - step_data_callback=AddExcludedObservationElements, - record_infos=False, - action_space=env.action_space, - observation_space=gym.spaces.Box(-np.inf, np.inf, (105,), np.float64), -) -# we do not observe `cfrc_ext` with the agent, but we keep it in the dataset observations -obs, _ = collector_env.reset(seed=SEED) - -# load policy model -# model is from: https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/tree/main/results/ant_v5_without_ctn_SAC/run_9 -model = SAC.load( - path="./best_model.zip", - env=env, - device="cpu", -) - - -for n_step in range(NUM_STEPS): - action, _ = model.predict(obs, deterministic=True) - - obs, rew, terminated, truncated, info = collector_env.step(action) - # Checkpoint - if (n_step + 1) % 200e3 == 0: - print(f"STEPS RECORDED: {n_step}") - - if terminated or truncated: - env.reset() - -dataset = collector_env.create_dataset( - dataset_id=DATASET_NAME, - eval_env="Ant-v5", - algorithm_name="SB3/SAC", - code_permalink="https://github.com/Farama-Foundation/minari-dataset-generation-scripts/tree/main/scripts/MuJoCo/Ant/expert_dataset.py", - author="Kallinteris Andreas", - author_email="kallinteris@protonmail.com", - description="Ant expert fine tuned policy, it was trained with `include_cfrc_ext_in_observation=False`, model training at https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation .", - requirements=["mujoco==3.2.3",], - # env_spec -) diff --git a/scripts/MuJoCo/Ant-expert-finetuned-v0/best_model.zip b/scripts/mujoco/ant/best_model.zip similarity index 100% rename from scripts/MuJoCo/Ant-expert-finetuned-v0/best_model.zip rename to scripts/mujoco/ant/best_model.zip diff --git a/scripts/mujoco/ant/create_dataset.py b/scripts/mujoco/ant/create_dataset.py new file mode 100644 index 0000000..f8b5f0f --- /dev/null +++ b/scripts/mujoco/ant/create_dataset.py @@ -0,0 +1,46 @@ +__credits__ = ["Kallinteris Andreas"] + +import gymnasium as gym +import minari +from minari import DataCollector +from stable_baselines3 import SAC + +SEED = 12345 +NUM_STEPS = int(2e6) + +DATASET_NAME = "mujoco/ant/expert-v0" +assert DATASET_NAME not in minari.list_local_datasets() + +env = gym.make("Ant-v5", max_episode_steps=1000) +collector_env = DataCollector( + env, + record_infos=False, +) +obs, _ = collector_env.reset(seed=SEED) + +# model is from: https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation/tree/main/results/ant_v5_without_ctn_SAC/run_9 +model = SAC.load( + path="./best_model.zip", + env=env, + device="cpu", +) + +for n_step in range(NUM_STEPS): + action, _ = model.predict(obs, deterministic=True) + + obs, rew, terminated, truncated, info = collector_env.step(action) + if (n_step + 1) % 200e3 == 0: + print(f"STEPS RECORDED: {n_step}") + + if terminated or truncated: + env.reset() + +dataset = collector_env.create_dataset( + dataset_id=DATASET_NAME, + algorithm_name="SB3/SAC", + code_permalink="https://github.com/Farama-Foundation/minari-dataset-generation-scripts/tree/main/scripts/MuJoCo/Ant/expert_dataset.py", + author="Kallinteris Andreas", + author_email="kallinteris@protonmail.com", + description="Ant expert fine tuned policy, model training at https://github.com/Kallinteris-Andreas/gymnasium-mujuco-v5-envs-validation .", + requirements=["mujoco==3.2.3",], +) diff --git a/scripts/MuJoCo/Ant-expert-finetuned-v0/requirements.txt b/scripts/mujoco/ant/requirements.txt similarity index 96% rename from scripts/MuJoCo/Ant-expert-finetuned-v0/requirements.txt rename to scripts/mujoco/ant/requirements.txt index 66fa5af..f5088ff 100644 --- a/scripts/MuJoCo/Ant-expert-finetuned-v0/requirements.txt +++ b/scripts/mujoco/ant/requirements.txt @@ -92,7 +92,7 @@ scipy==1.14.1 setuptools==75.1.0 shellingham==1.5.4 six==1.16.0 -stable-baselines3=git+https://github.com/pseudo-rnd-thoughts/stable-baselines3.git@2f6246033cb9111ad0d0df9d9bd98555957e08b5#egg=stable_baselines3 +stable-baselines3@git+https://github.com/pseudo-rnd-thoughts/stable-baselines3.git@2f6246033cb9111ad0d0df9d9bd98555957e08b5#egg=stable_baselines3 swig==4.2.1 sympy==1.13.3 tensorstore==0.1.66 From a953a1b25d440c6810c5b5cf18e4fa3b51bb3322 Mon Sep 17 00:00:00 2001 From: Omar Younis Date: Mon, 14 Oct 2024 00:10:17 +0200 Subject: [PATCH 14/22] restore step_data_callback --- scripts/mujoco/ant/create_dataset.py | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/scripts/mujoco/ant/create_dataset.py b/scripts/mujoco/ant/create_dataset.py index f8b5f0f..45aeda5 100644 --- a/scripts/mujoco/ant/create_dataset.py +++ b/scripts/mujoco/ant/create_dataset.py @@ -8,12 +8,28 @@ SEED = 12345 NUM_STEPS = int(2e6) + +class AddExcludedObservationElements(StepDataCallback): + """Add Excluded observation elements like cfrc_ext to the observation space.""" + + def __call__(self, env, **kwargs): + step_data = super().__call__(env, **kwargs) + # if getattr(env, "_include_cinert_in_observation", None) is False: + # if getattr(env, "_include_cvel_in_observation ", None) is False: + # if getattr(env, "_include_qfrc_actuator_in_observation ", None) is False: + if env.unwrapped._include_cfrc_ext_in_observation is False: + step_data["observation"] = np.concatenate( + [step_data["observation"], env.unwrapped.contact_forces[1:].flat.copy()] + ) + + return step_data + DATASET_NAME = "mujoco/ant/expert-v0" assert DATASET_NAME not in minari.list_local_datasets() env = gym.make("Ant-v5", max_episode_steps=1000) -collector_env = DataCollector( - env, +collector_env = DataCollector(env, + step_data_callback=AddExcludedObservationElements, record_infos=False, ) obs, _ = collector_env.reset(seed=SEED) From 01fcfac557d588e7a661e69631825610b52ceb18 Mon Sep 17 00:00:00 2001 From: Omar Younis Date: Mon, 14 Oct 2024 10:33:05 +0200 Subject: [PATCH 15/22] restore import --- scripts/mujoco/ant/create_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/mujoco/ant/create_dataset.py b/scripts/mujoco/ant/create_dataset.py index 45aeda5..9e431b8 100644 --- a/scripts/mujoco/ant/create_dataset.py +++ b/scripts/mujoco/ant/create_dataset.py @@ -2,7 +2,7 @@ import gymnasium as gym import minari -from minari import DataCollector +from minari import DataCollector, StepDataCallback from stable_baselines3 import SAC SEED = 12345 From 2604dc93450e4c417af066111255138e29a298d8 Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Tue, 15 Oct 2024 09:44:58 +0300 Subject: [PATCH 16/22] Update and rename create_dataset.py to expert_dataset.py --- .../ant/{create_dataset.py => expert_dataset.py} | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) rename scripts/mujoco/ant/{create_dataset.py => expert_dataset.py} (86%) diff --git a/scripts/mujoco/ant/create_dataset.py b/scripts/mujoco/ant/expert_dataset.py similarity index 86% rename from scripts/mujoco/ant/create_dataset.py rename to scripts/mujoco/ant/expert_dataset.py index 9e431b8..63942da 100644 --- a/scripts/mujoco/ant/create_dataset.py +++ b/scripts/mujoco/ant/expert_dataset.py @@ -2,6 +2,7 @@ import gymnasium as gym import minari +import numpy as np from minari import DataCollector, StepDataCallback from stable_baselines3 import SAC @@ -27,10 +28,14 @@ def __call__(self, env, **kwargs): DATASET_NAME = "mujoco/ant/expert-v0" assert DATASET_NAME not in minari.list_local_datasets() -env = gym.make("Ant-v5", max_episode_steps=1000) -collector_env = DataCollector(env, +env = gym.make("Ant-v5", include_cfrc_ext_in_observation=False, max_episode_steps=1000) +env.spec.kwargs = {} # overwrite the spec for the dataset since we include the observations with the callback + +collector_env = DataCollector( + env, step_data_callback=AddExcludedObservationElements, record_infos=False, + observation_space=gym.spaces.Box(-np.inf, np.inf, (105,), np.float64), ) obs, _ = collector_env.reset(seed=SEED) @@ -51,6 +56,7 @@ def __call__(self, env, **kwargs): if terminated or truncated: env.reset() + dataset = collector_env.create_dataset( dataset_id=DATASET_NAME, algorithm_name="SB3/SAC", From a2d68e0d1490bf718e4d2372417bb0000639145e Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Sun, 20 Oct 2024 17:38:28 +0300 Subject: [PATCH 17/22] add MuJoCo script --- scripts/mujoco/create_dataset.py | 112 +++++++++ scripts/mujoco/evaluate_policy.py | 28 +++ scripts/mujoco/requirements.txt | 138 +++++++++++ scripts/mujoco/train.py | 365 ++++++++++++++++++++++++++++++ 4 files changed, 643 insertions(+) create mode 100644 scripts/mujoco/create_dataset.py create mode 100644 scripts/mujoco/evaluate_policy.py create mode 100644 scripts/mujoco/requirements.txt create mode 100644 scripts/mujoco/train.py diff --git a/scripts/mujoco/create_dataset.py b/scripts/mujoco/create_dataset.py new file mode 100644 index 0000000..75028f8 --- /dev/null +++ b/scripts/mujoco/create_dataset.py @@ -0,0 +1,112 @@ +import gymnasium as gym +import minari +from huggingface_sb3 import load_from_hub +from stable_baselines3 import PPO, SAC, TD3 +from tqdm import tqdm + +ENV_IDS = [ + ("InvertedPendulum", ("medium", "expert"), 100_000, "SAC"), + ("InvertedDoublePendulum", ("medium", "expert"), 100_000, "SAC"), + ("Reacher", ("medium", "expert"), 500_000, "SAC"), + ("Pusher", ("medium", "expert"), 500_000, "SAC"), + ("HalfCheetah", ("simple", "medium", "expert"), 1_000_000, "SAC"), + ("Hopper", ("simple", "medium", "expert"), 1_000_000, "SAC"), + ("Walker2d", ("simple", "medium", "expert"), 1_000_000, "SAC"), + ("Swimmer", ("simple", "medium", "expert"), 1_000_000, "SAC"), + ("Ant", ("simple", "medium"), 1_000_000, "SAC"), + # ("Humanoid", ("simple", "medium"), 1_000_000, "SAC"), + # ("HumanoidStandup", ("simple", "medium"), 1_000_000, "SAC"), +] + + +def create_dataset_from_policy(dataset_id, collector_env, policy, n_steps: int, algorithm_name): + truncated = True + terminated = True + seed = 123 + for _ in tqdm(range(n_steps)): + if terminated or truncated: + obs, _ = env.reset(seed=seed) + seed += 1 + action = policy(obs) + obs, _, terminated, truncated, _ = env.step(action) + + return collector_env.create_dataset( + dataset_id=f"mujoco/{dataset_id}", + algorithm_name="SB3/{algorithm_name}", + code_permalink="https://github.com/Farama-Foundation/minari-dataset-generation-scripts", + author="Kallinteris Andreas", + author_email="kallinteris@protonmail.com", + requirements=[ + "mujoco==3.2.3", + ], + ) + + +def load_policy(env_id: str, algo: str, proficiency: str): + model_checkpoint = load_from_hub( + repo_id=f"farama-minari/{env_id}-v5-{algo.upper()}-{proficiency}", + filename=f"{env_id.lower()}-v5-{algo.lower()}-{proficiency}.zip", + ) + + match algo: + case "SAC": + policy = SAC.load(model_checkpoint) + case "TD3": + policy = TD3.load(model_checkpoint) + case "PPO": + policy = PPO.load(model_checkpoint) + + return policy + + +if __name__ == "__main__": + for env_run_spec in ENV_IDS: + # unpack dataset spec + env_id = env_run_spec[0] + proficiencies = env_run_spec[1] + n_steps = env_run_spec[2] + algo = env_run_spec[3] + + + # make datasets + print(f"\nCREATING EXPERT DATASET FOR {env_id}") + if "expert" in proficiencies: + env = gym.make(f"{env_id}-v5") + env = minari.DataCollector(env, record_infos=False) # TODO record_info? + + expert_policy = load_policy(env_id, algo, "expert") + expert_dataset = create_dataset_from_policy( + f"{env_id.lower()}/expert-v0", + env, + lambda x: expert_policy.predict(x)[0], + n_steps=n_steps, + algo, + ) + + print(f"\nCREATING MEDIUM DATASET FOR {env_id}") + if "expert" in proficiencies: + env = gym.make(f"{env_id}-v5") + env = minari.DataCollector(env, record_infos=False) # TODO record_info? + + medium_policy = load_policy(env_id, algo, "expert") + medium_dataset = create_dataset_from_policy( + f"{env_id.lower()}/medium-v0", + env, + lambda x: medium_policy.predict(x)[0], + n_steps=n_steps, + algo, + ) + + print(f"\nCREATING SIMPLE DATASET FOR {env_id}") + if "simple" in proficiencies: + env = gym.make(f"{env_id}-v5") + env = minari.DataCollector(env, record_infos=False) # TODO record_info? + + simple_policy = load_policy(env_id, algo, "expert") + simple_dataset = create_dataset_from_policy( + f"{env_id.lower()}/simple-v0", + env, + lambda x: simple_policy.predict(x)[0], + n_steps=n_steps, + algo, + ) diff --git a/scripts/mujoco/evaluate_policy.py b/scripts/mujoco/evaluate_policy.py new file mode 100644 index 0000000..583f26c --- /dev/null +++ b/scripts/mujoco/evaluate_policy.py @@ -0,0 +1,28 @@ +import gymnasium as gym +from huggingface_sb3 import load_from_hub +from stable_baselines3 import SAC +from stable_baselines3.common.evaluation import evaluate_policy + +ENV_ID = "HalfCheetah" +ALGORITHM = "SAC" +PROFICIENCY = "medium" + +eval_env = gym.make(f"{ENV_ID}-v5") + +match ALGORITHM: + case "SAC": + expert_model_checkpoint = load_from_hub( + repo_id=f"farama-minari/{ENV_ID}-v5-{ALGORITHM.upper()}-{PROFICIENCY}", + filename=f"{ENV_ID.lower()}-v5-{ALGORITHM.lower()}-{PROFICIENCY}.zip", + ) + model = SAC.load(expert_model_checkpoint) + case "TD3": + None + case "PPO": + None +print("MODEL LOADED") + + +mean_reward, std_reward = evaluate_policy(model, eval_env, render=False, n_eval_episodes=1000, deterministic=True, warn=False) +print(f"{ENV_ID}-v5/{PROFICIENCY}/{ALGORITHM}") +print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") diff --git a/scripts/mujoco/requirements.txt b/scripts/mujoco/requirements.txt new file mode 100644 index 0000000..8bd3ac6 --- /dev/null +++ b/scripts/mujoco/requirements.txt @@ -0,0 +1,138 @@ +absl-py==2.1.0 +ale-py==0.10.1 +annoy==1.17.3 +blitzdb==0.4.4 +bottle==0.13.2 +box2d-py==2.3.5 +cachetools==5.5.0 +certifi==2024.8.30 +cffi==1.17.1 +charset-normalizer==3.4.0 +chex==0.1.87 +click==8.1.7 +cloudpickle==3.1.0 +colorlover==0.3.0 +contourpy==1.3.0 +cycler==0.12.1 +Cython==0.29.37 +decorator==4.4.2 +docker-pycreds==0.4.0 +etils==1.9.4 +Farama-Notifications==0.0.4 +fasteners==0.19 +filelock==3.16.1 +flax==0.9.0 +fonttools==4.54.1 +fsspec==2024.9.0 +gitdb==4.0.11 +GitPython==3.1.43 +glfw==2.7.0 +google-api-core==2.21.0 +google-auth==2.35.0 +google-cloud-core==2.4.1 +google-cloud-storage==2.18.2 +google-crc32c==1.6.0 +google-resumable-media==2.7.2 +googleapis-common-protos==1.65.0 +grpcio==1.66.2 +gymnasium==1.0.0 +h5py==3.12.1 +huggingface-hub==0.25.2 +huggingface-sb3==3.0 +humanize==4.11.0 +idna==3.10 +imageio==2.35.1 +imageio-ffmpeg==0.5.1 +importlib_resources==6.4.5 +jax==0.4.34 +jaxlib==0.4.34 +Jinja2==3.1.4 +joblib==1.4.2 +kiwisolver==1.4.7 +lazy_loader==0.4 +lime==0.2.0.1 +Markdown==3.7 +markdown-it-py==3.0.0 +MarkupSafe==3.0.1 +matplotlib==3.9.2 +mdurl==0.1.2 +minari==0.5.1 +ml_dtypes==0.5.0 +moviepy==1.0.3 +mpmath==1.3.0 +msgpack==1.1.0 +mujoco==3.2.3 +mujoco-py==2.1.2.14 +nest-asyncio==1.6.0 +networkx==3.4.1 +numpy==1.26.4 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-nccl-cu12==2.20.5 +nvidia-nvjitlink-cu12==12.6.77 +nvidia-nvtx-cu12==12.1.105 +opencv-python==4.10.0.84 +opt_einsum==3.4.0 +optax==0.2.3 +orbax-checkpoint==0.7.0 +packaging==24.1 +pandas==2.2.3 +pillow==10.4.0 +platformdirs==4.3.6 +plotly==5.24.1 +proglog==0.1.10 +proto-plus==1.24.0 +protobuf==5.28.2 +psutil==6.0.0 +pyarrow==17.0.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycparser==2.22 +pygame==2.6.1 +Pygments==2.18.0 +PyOpenGL==3.1.7 +pyparsing==3.1.4 +python-dateutil==2.9.0.post0 +pytz==2024.2 +PyYAML==6.0.2 +reach==4.1.1 +requests==2.32.3 +rich==13.9.2 +rsa==4.9 +scikit-image==0.24.0 +scikit-learn==1.5.2 +scipy==1.14.1 +sentry-sdk==2.16.0 +setproctitle==1.3.3 +setuptools==75.1.0 +shellingham==1.5.4 +six==1.16.0 +smmap==5.0.1 +stable_baselines3@git+https://github.com/pseudo-rnd-thoughts/stable-baselines3.git@2f6246033cb9111ad0d0df9d9bd98555957e08b5#egg=stable_baselines3 +swig==4.2.1 +sympy==1.13.3 +tenacity==9.0.0 +tensorboard==2.18.0 +tensorboard-data-server==0.7.2 +tensorstore==0.1.66 +threadpoolctl==3.5.0 +tifffile==2024.9.20 +toolz==1.0.0 +torch==2.4.1 +tqdm==4.66.5 +triton==3.0.0 +typer==0.12.5 +typing_extensions==4.12.2 +tzdata==2024.2 +urllib3==2.2.3 +wandb==0.18.3 +wasabi==1.1.3 +Werkzeug==3.0.4 +zipp==3.20.2 diff --git a/scripts/mujoco/train.py b/scripts/mujoco/train.py new file mode 100644 index 0000000..8c582f2 --- /dev/null +++ b/scripts/mujoco/train.py @@ -0,0 +1,365 @@ +from typing import Any, Dict, List + +import gymnasium as gym +import numpy as np +from gymnasium.wrappers import RecordVideo +from huggingface_sb3 import package_to_hub +from stable_baselines3 import PPO, SAC, TD3 +from stable_baselines3.common.buffers import ReplayBuffer +from stable_baselines3.common.callbacks import CheckpointCallback +from stable_baselines3.common.env_util import make_vec_env +from stable_baselines3.common.monitor import Monitor +from wandb.integration.sb3 import WandbCallback + +import wandb + +# NOTE: different timesteps are used for different environments/proficiencies, check `config.yaml` for indivigual values + +ALGORITHM = "ppo" +PROFICIENCY = "expert" +TIMESTEPS = 10000_000 +# ["HalfCheetah", "Ant", "Hopper", "Walker2d", "InvertedPendulum", "InvertedDoublePendulum", "Reacher", "Pusher", "Swimmer"] +ENV_LIST = ["HumanoidStandup"] +RUNS = 1 + +print(f"Training {ENV_LIST}/{PROFICIENCY} - {ALGORITHM}/{TIMESTEPS}") + +INFO_KEYS = { + "HalfCheetah": { + "x_position", + "x_velocity", + "reward_forward", + "reward_ctrl", + }, + "Hopper": { + "x_position", + "x_velocity", + "z_distance_from_origin", + "reward_forward", + "reward_ctrl", + "reward_survive", + }, + "Ant": { + "x_position", + "y_position", + "distance_from_origin", + "x_velocity", + "y_velocity", + "reward_forward", + "reward_ctrl", + "reward_survive", + "reward_contact", + }, + "Walker2d": { + "x_position", + "x_velocity", + "z_distance_from_origin", + "reward_forward", + "reward_ctrl", + "reward_survive", + }, + "InvertedPendulum": { + "reward_survive", + }, + "InvertedDoublePendulum": { + "reward_survive", + "distance_penalty", + "velocity_penalty", + }, + "Pusher": { + "reward_dist", + "reward_ctrl", + "reward_near", + }, + "Reacher": { + "reward_dist", + "reward_ctrl", + }, + "Swimmer": { + "x_position", + "y_position", + "distance_from_origin", + "x_velocity", + "y_velocity", + "reward_forward", + "reward_ctrl", + }, + "Humanoid": { + "x_position", + "y_position", + # "tendon_length", + # "tendon_velocity", + "distance_from_origin", + "x_velocity", + "y_velocity", + "reward_survive", + "reward_forward", + "reward_ctrl", + "reward_contact", + }, + "HumanoidStandup": { + "x_position", + "y_position", + "z_distance_from_origin", + # "tendon_length", + # "tendon_velocity", + "distance_from_origin", + "reward_linup", + "reward_quadctrl", + "reward_impact", + }, +} + + +def gen_learning_start(env_id: str): + if env_id == "InvertedPendulum": + return 100 + elif env_id == "InvertedDoublePendulum": + return 1000 + + return 10000 + + +def gen_gamma(env_id: str): + if env_id == "Swimmer": + return 0.9999 + return 0.99 + + +class InfoReplayBuffer(ReplayBuffer): + """Extend SB3 ReplayBuffer to store the info return. + Similar to HerReplayBuffer. + """ + + def __init__(self, buffer_size, observation_space, action_space, info_keys: set, **kwargs): + super().__init__(buffer_size, observation_space, action_space, **kwargs) + # Add infos buffer + # All of the Mujoco environments have the same following space for their info keys: spaces.Box(low=-np.inf, high=np.inf, shape=(1,), dtype=np.float64) + self.infos = {key: np.zeros((self.buffer_size, self.n_envs), dtype=np.float64) for key in info_keys} + + self.ep_start = np.zeros((self.buffer_size, self.n_envs), dtype=np.int64) + self.ep_length = np.zeros((self.buffer_size, self.n_envs), dtype=np.int64) + self._current_ep_start = np.zeros(self.n_envs, dtype=np.int64) + + def add( + self, + obs: np.ndarray, + next_obs: np.ndarray, + action: np.ndarray, + reward: np.ndarray, + done: np.ndarray, + infos: List[Dict[str, Any]], + ) -> None: + + # Update episode start + self.ep_start[self.pos] = self._current_ep_start.copy() + + # print(f"PPP {self.pos}") + for key in self.infos.keys(): + # print(f"KEY {key}") + # if key == "tendon_velocity": + # breakpoint() + self.infos[key][self.pos] = np.array([info.get(key, 0.0) for info in infos]) + + super().add(obs, next_obs, action, reward, done, infos) + + # When episode ends, compute and store the episode length + for env_idx in range(self.n_envs): + if done[env_idx]: + self._compute_episode_length(env_idx) + + def _compute_episode_length(self, env_idx: int) -> None: + """ + Compute and store the episode length for environment with index env_idx + :param env_idx: index of the environment for which the episode length should be computed + """ + episode_start = self._current_ep_start[env_idx] + episode_end = self.pos + if episode_end < episode_start: + # Occurs when the buffer becomes full, the storage resumes at the + # beginning of the buffer. This can happen in the middle of an episode. + episode_end += self.buffer_size + episode_indices = np.arange(episode_start, episode_end) % self.buffer_size + self.ep_length[episode_indices, env_idx] = episode_end - episode_start + # Update the current episode start + self._current_ep_start[env_idx] = self.pos + + def truncate_last_trajectory(self) -> None: + """ + If called, we assume that the last trajectory in the replay buffer was finished + (and truncate it). + If not called, we assume that we continue the same trajectory (same episode). + """ + # If we are at the start of an episode, no need to truncate + if (self._current_ep_start != self.pos).any(): + # only consider epsiodes that are not finished + for env_idx in np.where(self._current_ep_start != self.pos)[0]: + self._compute_episode_length(env_idx) + + self.timeouts[self.pos - 1, env_idx] = True # not an actual timeout, but it allows bootstrapping + + def convert_to_minari_buffer(self) -> List[Dict[str, np.ndarray]]: + # Auto-truncate if last episode didn't finish + self.truncate_last_trajectory() + + minari_buffer = [] + for env_id in range(self.n_envs): + ep_start = 0 + while True: + ep_length = self.ep_length[ep_start, env_id] + if ep_length == 0: + # No more trajectories were collected for this env + break + ep_end = ep_start + ep_length + minari_buffer.append( + { + "observations": np.concatenate( + ( + self.observations[ep_start:ep_end, env_id], + np.expand_dims(self.next_observations[ep_end - 1, env_id], axis=0), + ), + axis=0, + ), + "actions": self.actions[ep_start:ep_end, env_id], + "rewards": self.rewards[ep_start:ep_end, env_id], + "terminations": self.dones[ep_start:ep_end, env_id], + "truncations": self.timeouts[ep_start:ep_end, env_id], + "infos": {key: np.pad(value[ep_start:ep_end, env_id], (1, 0)) for key, value in self.infos.items()}, + } + ) + + ep_start = ep_end + + return minari_buffer + + +def make_env(env_id: str, run_name: str): + """Wrapper to create the appropriate environment.""" + env = gym.make( + f"{env_id}-v5", + render_mode="rgb_array", + ) + env = RecordVideo(env, f"videos/{run_name}") + env = Monitor(env) + return env + + +if __name__ == "__main__": + for env_id in ENV_LIST: + for seed in range(RUNS): + run_name = f"{env_id}-SAC-{seed}" + n_envs = 5 + # Set up WandB run + run = wandb.init( + project="Minari", + name=run_name, + # config=config, + sync_tensorboard=True, + monitor_gym=True, + save_code=True, + ) + + env = make_vec_env(make_env, n_envs=n_envs, env_kwargs={"env_id": env_id, "run_name": run_name}) + + if ALGORITHM == "sac": + model = SAC( + "MlpPolicy", + env, + tensorboard_log=f"runs/{run.id}", + learning_starts=gen_learning_start(env_id), + gamma=gen_gamma(env_id), + use_sde=False, + seed=seed, + replay_buffer_class=InfoReplayBuffer, + replay_buffer_kwargs={"info_keys": INFO_KEYS[env_id]}, + ) + elif ALGORITHM == "td3": + model = TD3( + "MlpPolicy", + env, + tensorboard_log=f"runs/{run.id}", + learning_starts=gen_learning_start(env_id), + gamma=gen_gamma(env_id), + seed=seed, + replay_buffer_class=InfoReplayBuffer, + replay_buffer_kwargs={"info_keys": INFO_KEYS[env_id]}, + ) + elif ALGORITHM == "ppo": + model = PPO( + "MlpPolicy", + env, + tensorboard_log=f"runs/{run.id}", + gamma=gen_gamma(env_id), + seed=seed, + ent_coef=5e-6, + device="cpu", + ) + + wandb_callback = WandbCallback() + # Save a checkpoint every 100000 steps + checkpoint_callback = CheckpointCallback( + save_freq=int(100000 / n_envs), + save_path=f"logs/{env_id}/", + name_prefix="rl_model", + # save_replay_buffer=True, + save_replay_buffer=False, + ) + + model.learn(total_timesteps=TIMESTEPS, callback=[wandb_callback, checkpoint_callback], log_interval=4) + model.save(f"{env_id.lower()}-v5-{ALGORITHM}-{PROFICIENCY}") + run.finish() + + print("FINISHED LEARNING") + print("FINISHED LEARNING") + print("FINISHED LEARNING") + + eval_env = make_vec_env(f"{env_id}-v5", n_envs=1) + + # Replace InfoReplayBuffer for sb3 ReplayBuffer + if ALGORITHM == "sac": + model = SAC.load( + f"{env_id.lower()}-v5-sac-{PROFICIENCY}", + custom_objects={"replay_buffer_class": ReplayBuffer, "replay_buffer_kwargs": {}, "lr_schedule": lambda _: 0.0}, + ) + elif ALGORITHM == "td3": + model = TD3.load( + f"{env_id.lower()}-v5-td3-{PROFICIENCY}", + custom_objects={"replay_buffer_class": ReplayBuffer, "replay_buffer_kwargs": {}, "lr_schedule": lambda _: 0.0}, + ) + elif ALGORITHM == "ppo": + model = PPO.load( + f"{env_id.lower()}-v5-ppo-{PROFICIENCY}", + custom_objects={"lr_schedule": lambda _: 0.0}, + ) + + package_to_hub( + model=model, + model_name=f"{env_id.lower()}-v5-{ALGORITHM}-{PROFICIENCY}", + model_architecture=ALGORITHM.upper(), + env_id=f"{env_id}-v5", + eval_env=eval_env, + repo_id=f"farama-minari/{env_id}-v5-{ALGORITHM.upper()}-{PROFICIENCY}", + commit_message="model", + ) + + print("FINISHED HF") + print("FINISHED HF") + print("FINISHED HF") + + """ + # Replace InfoReplayBuffer for sb3 ReplayBuffer + model = SAC.load( + f"logs/{env_id}/rl_model_200000_steps.zip", + custom_objects={"replay_buffer_class": ReplayBuffer, "replay_buffer_kwargs": {}, "lr_schedule": lambda _: 0.0}, + ) + + package_to_hub( + model=model, + model_name=f"{env_id.lower()}-v5-{ALGORITHM}-medium", + model_architecture=ALGORITHM.upper(), + env_id=f"{env_id}-v5", + eval_env=eval_env, + repo_id=f"farama-minari/{env_id}-v5-{}-medium", + commit_message="model", + ) + """ From ff8e935f8826ee72b239c519c9c7e907500aaab2 Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Mon, 21 Oct 2024 11:15:01 +0300 Subject: [PATCH 18/22] update --- scripts/mujoco/create_dataset.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/scripts/mujoco/create_dataset.py b/scripts/mujoco/create_dataset.py index 75028f8..4fe4966 100644 --- a/scripts/mujoco/create_dataset.py +++ b/scripts/mujoco/create_dataset.py @@ -9,7 +9,7 @@ ("InvertedDoublePendulum", ("medium", "expert"), 100_000, "SAC"), ("Reacher", ("medium", "expert"), 500_000, "SAC"), ("Pusher", ("medium", "expert"), 500_000, "SAC"), - ("HalfCheetah", ("simple", "medium", "expert"), 1_000_000, "SAC"), + #("HalfCheetah", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Hopper", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Walker2d", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Swimmer", ("simple", "medium", "expert"), 1_000_000, "SAC"), @@ -23,10 +23,13 @@ def create_dataset_from_policy(dataset_id, collector_env, policy, n_steps: int, truncated = True terminated = True seed = 123 - for _ in tqdm(range(n_steps)): + for step in tqdm(range(n_steps)): if terminated or truncated: obs, _ = env.reset(seed=seed) seed += 1 + if (n_steps - step) < 1000: # trim trailing non-full episodes + break + action = policy(obs) obs, _, terminated, truncated, _ = env.step(action) @@ -79,7 +82,7 @@ def load_policy(env_id: str, algo: str, proficiency: str): f"{env_id.lower()}/expert-v0", env, lambda x: expert_policy.predict(x)[0], - n_steps=n_steps, + n_steps, algo, ) @@ -93,7 +96,7 @@ def load_policy(env_id: str, algo: str, proficiency: str): f"{env_id.lower()}/medium-v0", env, lambda x: medium_policy.predict(x)[0], - n_steps=n_steps, + n_steps, algo, ) @@ -107,6 +110,6 @@ def load_policy(env_id: str, algo: str, proficiency: str): f"{env_id.lower()}/simple-v0", env, lambda x: simple_policy.predict(x)[0], - n_steps=n_steps, + n_steps, algo, ) From 3fefb6c6e22866d427a31180aa5ea70577226b7f Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Mon, 21 Oct 2024 11:25:10 +0300 Subject: [PATCH 19/22] Update create_dataset.py --- scripts/mujoco/create_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/mujoco/create_dataset.py b/scripts/mujoco/create_dataset.py index 4fe4966..491f4d9 100644 --- a/scripts/mujoco/create_dataset.py +++ b/scripts/mujoco/create_dataset.py @@ -9,7 +9,7 @@ ("InvertedDoublePendulum", ("medium", "expert"), 100_000, "SAC"), ("Reacher", ("medium", "expert"), 500_000, "SAC"), ("Pusher", ("medium", "expert"), 500_000, "SAC"), - #("HalfCheetah", ("simple", "medium", "expert"), 1_000_000, "SAC"), + ("HalfCheetah", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Hopper", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Walker2d", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Swimmer", ("simple", "medium", "expert"), 1_000_000, "SAC"), From e094e74f720e8094de8064f8c06de5a4994141ab Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Mon, 21 Oct 2024 11:44:55 +0300 Subject: [PATCH 20/22] cleanup --- scripts/mujoco/train.py | 30 ++++-------------------------- 1 file changed, 4 insertions(+), 26 deletions(-) diff --git a/scripts/mujoco/train.py b/scripts/mujoco/train.py index 8c582f2..484bca5 100644 --- a/scripts/mujoco/train.py +++ b/scripts/mujoco/train.py @@ -87,8 +87,8 @@ "Humanoid": { "x_position", "y_position", - # "tendon_length", - # "tendon_velocity", + # "tendon_length", # Tendons are not kept in info for compatability + # "tendon_velocity", # Tendons are not kept in info for compatability "distance_from_origin", "x_velocity", "y_velocity", @@ -101,8 +101,8 @@ "x_position", "y_position", "z_distance_from_origin", - # "tendon_length", - # "tendon_velocity", + # "tendon_length", # Tendons are not kept in info for compatability + # "tendon_velocity", # Tendons are not kept in info for compatability "distance_from_origin", "reward_linup", "reward_quadctrl", @@ -310,8 +310,6 @@ def make_env(env_id: str, run_name: str): run.finish() print("FINISHED LEARNING") - print("FINISHED LEARNING") - print("FINISHED LEARNING") eval_env = make_vec_env(f"{env_id}-v5", n_envs=1) @@ -343,23 +341,3 @@ def make_env(env_id: str, run_name: str): ) print("FINISHED HF") - print("FINISHED HF") - print("FINISHED HF") - - """ - # Replace InfoReplayBuffer for sb3 ReplayBuffer - model = SAC.load( - f"logs/{env_id}/rl_model_200000_steps.zip", - custom_objects={"replay_buffer_class": ReplayBuffer, "replay_buffer_kwargs": {}, "lr_schedule": lambda _: 0.0}, - ) - - package_to_hub( - model=model, - model_name=f"{env_id.lower()}-v5-{ALGORITHM}-medium", - model_architecture=ALGORITHM.upper(), - env_id=f"{env_id}-v5", - eval_env=eval_env, - repo_id=f"farama-minari/{env_id}-v5-{}-medium", - commit_message="model", - ) - """ From 402eab17a810080dfb8d74d6ca4c814eacccdb67 Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas <30759571+Kallinteris-Andreas@users.noreply.github.com> Date: Mon, 21 Oct 2024 12:10:47 +0300 Subject: [PATCH 21/22] fix trailing triming for pusher and reacher --- scripts/mujoco/create_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/mujoco/create_dataset.py b/scripts/mujoco/create_dataset.py index 491f4d9..0e1b139 100644 --- a/scripts/mujoco/create_dataset.py +++ b/scripts/mujoco/create_dataset.py @@ -27,7 +27,7 @@ def create_dataset_from_policy(dataset_id, collector_env, policy, n_steps: int, if terminated or truncated: obs, _ = env.reset(seed=seed) seed += 1 - if (n_steps - step) < 1000: # trim trailing non-full episodes + if (n_steps - step) < collector_env.spec.max_episode_steps : # trim trailing non-full episodes break action = policy(obs) From 167f9097235f17f228a056f25ee2b6d8a270e572 Mon Sep 17 00:00:00 2001 From: Kallinteris Andreas Date: Mon, 4 Nov 2024 14:52:05 +0200 Subject: [PATCH 22/22] final version --- scripts/mujoco/create_dataset.py | 12 +- .../mujoco/create_dataset_humanoid_standup.py | 142 ++++++++++++++++++ scripts/mujoco/evaluate_dataset.py | 6 + scripts/mujoco/evaluate_policy.py | 6 +- scripts/{mujoco => }/train.py | 101 +++++++------ 5 files changed, 211 insertions(+), 56 deletions(-) create mode 100644 scripts/mujoco/create_dataset_humanoid_standup.py create mode 100644 scripts/mujoco/evaluate_dataset.py rename scripts/{mujoco => }/train.py (81%) diff --git a/scripts/mujoco/create_dataset.py b/scripts/mujoco/create_dataset.py index 0e1b139..dbab101 100644 --- a/scripts/mujoco/create_dataset.py +++ b/scripts/mujoco/create_dataset.py @@ -12,10 +12,10 @@ ("HalfCheetah", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Hopper", ("simple", "medium", "expert"), 1_000_000, "SAC"), ("Walker2d", ("simple", "medium", "expert"), 1_000_000, "SAC"), - ("Swimmer", ("simple", "medium", "expert"), 1_000_000, "SAC"), + ("Swimmer", ("expert"), 1_000_000, "SAC"), ("Ant", ("simple", "medium"), 1_000_000, "SAC"), - # ("Humanoid", ("simple", "medium"), 1_000_000, "SAC"), - # ("HumanoidStandup", ("simple", "medium"), 1_000_000, "SAC"), + ("Humanoid", ("simple", "medium", "expert"), 1_000_000, "SAC"), + #("HumanoidStandup", ("simple", "medium", "expert"), 1_000_000, "SAC"), ] @@ -87,11 +87,11 @@ def load_policy(env_id: str, algo: str, proficiency: str): ) print(f"\nCREATING MEDIUM DATASET FOR {env_id}") - if "expert" in proficiencies: + if "medium" in proficiencies: env = gym.make(f"{env_id}-v5") env = minari.DataCollector(env, record_infos=False) # TODO record_info? - medium_policy = load_policy(env_id, algo, "expert") + medium_policy = load_policy(env_id, algo, "medium") medium_dataset = create_dataset_from_policy( f"{env_id.lower()}/medium-v0", env, @@ -105,7 +105,7 @@ def load_policy(env_id: str, algo: str, proficiency: str): env = gym.make(f"{env_id}-v5") env = minari.DataCollector(env, record_infos=False) # TODO record_info? - simple_policy = load_policy(env_id, algo, "expert") + simple_policy = load_policy(env_id, algo, "simple") simple_dataset = create_dataset_from_policy( f"{env_id.lower()}/simple-v0", env, diff --git a/scripts/mujoco/create_dataset_humanoid_standup.py b/scripts/mujoco/create_dataset_humanoid_standup.py new file mode 100644 index 0000000..ef74020 --- /dev/null +++ b/scripts/mujoco/create_dataset_humanoid_standup.py @@ -0,0 +1,142 @@ +import gymnasium as gym +import minari +import numpy as np +from huggingface_sb3 import load_from_hub +from stable_baselines3 import PPO, SAC, TD3 +from tqdm import tqdm + +ENV_IDS = [ + ("HumanoidStandup", ("simple", "medium", "expert"), 1_000_000, "SAC"), +] + +class AddExcludedObservationElements(minari.StepDataCallback): + """Add Excluded observation elements like `cfrc_ext` to the observation space.""" + + def __call__(self, env, **kwargs): + step_data = super().__call__(env, **kwargs) + if getattr(env.unwrapped, "_include_cinert_in_observation", True) is False: + step_data["observation"] = np.concatenate( + [step_data["observation"], env.unwrapped.data.cinert[1:].flatten()] + ) + if getattr(env.unwrapped, "_include_cvel_in_observation", True) is False: + step_data["observation"] = np.concatenate( + [step_data["observation"], env.unwrapped.data.cvel[1:].flatten()] + ) + if getattr(env.unwrapped, "_include_qfrc_actuator_in_observation", True) is False: + step_data["observation"] = np.concatenate( + [step_data["observation"], env.unwrapped.data.qfrc_actuator[6:].flatten()] + ) + if getattr(env.unwrapped, "_include_cfrc_ext_in_observation", True) is False: + step_data["observation"] = np.concatenate( + [step_data["observation"], env.unwrapped.data.cfrc_ext[1:].flat.copy()] + ) + + return step_data + + +def create_dataset_from_policy(dataset_id, collector_env, policy, n_steps: int, algorithm_name): + truncated = True + terminated = True + seed = 123 + for step in tqdm(range(n_steps)): + if terminated or truncated: + obs, _ = env.reset(seed=seed) + seed += 1 + if (n_steps - step) < collector_env.spec.max_episode_steps : # trim trailing non-full episodes + break + + action = policy(obs) + obs, _, terminated, truncated, _ = env.step(action) + + return collector_env.create_dataset( + dataset_id=f"mujoco/{dataset_id}", + algorithm_name="SB3/{algorithm_name}", + code_permalink="https://github.com/Farama-Foundation/minari-dataset-generation-scripts", + author="Kallinteris Andreas", + author_email="kallinteris@protonmail.com", + requirements=[ + "mujoco==3.2.3", + ], + ) + + +def load_policy(env_id: str, algo: str, proficiency: str): + model_checkpoint = load_from_hub( + repo_id=f"farama-minari/{env_id}-v5-{algo.upper()}-{proficiency}", + filename=f"{env_id.lower()}-v5-{algo.lower()}-{proficiency}.zip", + ) + + match algo: + case "SAC": + policy = SAC.load(model_checkpoint) + case "TD3": + policy = TD3.load(model_checkpoint) + case "PPO": + policy = PPO.load(model_checkpoint) + + return policy + + +def make_env(env_id: str): + """Wrapper to create the appropriate environment.""" + if env_id == "HumanoidStandup": + env = gym.make(f"{env_id}-v5", include_cinert_in_observation=False, include_cvel_in_observation=False, include_qfrc_actuator_in_observation=False, include_cfrc_ext_in_observation=False) + else: + env = gym.make(f"{env_id}-v5", render_mode="rgb_array",) + return env + + +if __name__ == "__main__": + for env_run_spec in ENV_IDS: + # unpack dataset spec + env_id = env_run_spec[0] + proficiencies = env_run_spec[1] + n_steps = env_run_spec[2] + algo = env_run_spec[3] + + + # make datasets + print(f"\nCREATING EXPERT DATASET FOR {env_id}") + if "expert" in proficiencies: + env = make_env(env_id) + env.spec.kwargs = {} # overwrite the spec for the dataset since we include the observations with the callback + env = minari.DataCollector(env, step_data_callback=AddExcludedObservationElements, record_infos=False, observation_space=gym.spaces.Box(-np.inf, np.inf, (348,), np.float64)) # TODO record_info? + + expert_policy = load_policy(env_id, algo, "expert") + expert_dataset = create_dataset_from_policy( + f"{env_id.lower()}/expert-v0", + env, + lambda x: expert_policy.predict(x)[0], + n_steps, + algo, + ) + + print(f"\nCREATING MEDIUM DATASET FOR {env_id}") + if "medium" in proficiencies: + env = make_env(env_id) + env.spec.kwargs = {} # overwrite the spec for the dataset since we include the observations with the callback + env = minari.DataCollector(env, step_data_callback=AddExcludedObservationElements, record_infos=False, observation_space=gym.spaces.Box(-np.inf, np.inf, (348,), np.float64)) # TODO record_info? + + medium_policy = load_policy(env_id, algo, "medium") + medium_dataset = create_dataset_from_policy( + f"{env_id.lower()}/medium-v0", + env, + lambda x: medium_policy.predict(x)[0], + n_steps, + algo, + ) + + print(f"\nCREATING SIMPLE DATASET FOR {env_id}") + if "simple" in proficiencies: + env = make_env(env_id) + env.spec.kwargs = {} # overwrite the spec for the dataset since we include the observations with the callback + env = minari.DataCollector(env, step_data_callback=AddExcludedObservationElements, record_infos=False, observation_space=gym.spaces.Box(-np.inf, np.inf, (348,), np.float64)) # TODO record_info? + + simple_policy = load_policy(env_id, algo, "simple") + simple_dataset = create_dataset_from_policy( + f"{env_id.lower()}/simple-v0", + env, + lambda x: simple_policy.predict(x)[0], + n_steps, + algo, + ) diff --git a/scripts/mujoco/evaluate_dataset.py b/scripts/mujoco/evaluate_dataset.py new file mode 100644 index 0000000..61a3ac7 --- /dev/null +++ b/scripts/mujoco/evaluate_dataset.py @@ -0,0 +1,6 @@ +import minari + +for dataset_id in minari.list_local_datasets().keys(): + ds = minari.load_dataset(dataset_id) + episodic_return = sum([ep.rewards.sum() / len(ds) for ep in ds]) + print(f"{dataset_id} - return: {episodic_return}") diff --git a/scripts/mujoco/evaluate_policy.py b/scripts/mujoco/evaluate_policy.py index 583f26c..8c3e53c 100644 --- a/scripts/mujoco/evaluate_policy.py +++ b/scripts/mujoco/evaluate_policy.py @@ -3,11 +3,13 @@ from stable_baselines3 import SAC from stable_baselines3.common.evaluation import evaluate_policy -ENV_ID = "HalfCheetah" +ENV_ID = "Ant" ALGORITHM = "SAC" -PROFICIENCY = "medium" +# PROFICIENCY = "medium" +PROFICIENCY = "expert-fine-tuned" eval_env = gym.make(f"{ENV_ID}-v5") +eval_env = gym.make(f"{ENV_ID}-v5", include_cfrc_ext_in_observation=False) match ALGORITHM: case "SAC": diff --git a/scripts/mujoco/train.py b/scripts/train.py similarity index 81% rename from scripts/mujoco/train.py rename to scripts/train.py index 484bca5..37bd2e2 100644 --- a/scripts/mujoco/train.py +++ b/scripts/train.py @@ -14,12 +14,12 @@ import wandb # NOTE: different timesteps are used for different environments/proficiencies, check `config.yaml` for indivigual values +HF_REPO = "farama-minari" -ALGORITHM = "ppo" -PROFICIENCY = "expert" -TIMESTEPS = 10000_000 -# ["HalfCheetah", "Ant", "Hopper", "Walker2d", "InvertedPendulum", "InvertedDoublePendulum", "Reacher", "Pusher", "Swimmer"] -ENV_LIST = ["HumanoidStandup"] +ALGORITHM = "sac" +PROFICIENCY = "medium" +TIMESTEPS = 20_000_000 +ENV_LIST = ["HalfCheetah", "Ant", "Hopper", "Walker2d", "InvertedPendulum", "InvertedDoublePendulum", "Reacher", "Pusher", "Swimmer", "Humanoid", "HumanoidStandup"] RUNS = 1 print(f"Training {ENV_LIST}/{PROFICIENCY} - {ALGORITHM}/{TIMESTEPS}") @@ -122,7 +122,7 @@ def gen_learning_start(env_id: str): def gen_gamma(env_id: str): if env_id == "Swimmer": - return 0.9999 + return 1.0 return 0.99 @@ -235,25 +235,59 @@ def convert_to_minari_buffer(self) -> List[Dict[str, np.ndarray]]: def make_env(env_id: str, run_name: str): """Wrapper to create the appropriate environment.""" - env = gym.make( - f"{env_id}-v5", - render_mode="rgb_array", - ) + if env_id == "HumanoidStandup": + env = gym.make(f"{env_id}-v5", render_mode="rgb_array", include_cinert_in_observation=False, include_cvel_in_observation=False, include_qfrc_actuator_in_observation=False, include_cfrc_ext_in_observation=False) + else: + env = gym.make(f"{env_id}-v5", render_mode="rgb_array",) env = RecordVideo(env, f"videos/{run_name}") env = Monitor(env) return env +def initialize_mode(algo_name): + if ALGORITHM == "sac": + model = SAC( + "MlpPolicy", + env, + tensorboard_log=f"runs/{run.id}", + learning_starts=gen_learning_start(env_id), + gamma=gen_gamma(env_id), + use_sde=False, + seed=seed, + replay_buffer_class=InfoReplayBuffer, + replay_buffer_kwargs={"info_keys": INFO_KEYS[env_id]}, + ) + elif ALGORITHM == "td3": + model = TD3( + "MlpPolicy", + env, + tensorboard_log=f"runs/{run.id}", + learning_starts=gen_learning_start(env_id), + gamma=gen_gamma(env_id), + seed=seed, + replay_buffer_class=InfoReplayBuffer, + replay_buffer_kwargs={"info_keys": INFO_KEYS[env_id]}, + ) + elif ALGORITHM == "ppo": + model = PPO( + "MlpPolicy", + env, + tensorboard_log=f"runs/{run.id}", + gamma=gen_gamma(env_id), + seed=seed, + ent_coef=5e-6, + device="cpu", + ) + if __name__ == "__main__": for env_id in ENV_LIST: for seed in range(RUNS): - run_name = f"{env_id}-SAC-{seed}" + run_name = f"{env_id}-{ALGORITHM.upper()}-{seed}" n_envs = 5 # Set up WandB run run = wandb.init( project="Minari", name=run_name, - # config=config, sync_tensorboard=True, monitor_gym=True, save_code=True, @@ -261,39 +295,7 @@ def make_env(env_id: str, run_name: str): env = make_vec_env(make_env, n_envs=n_envs, env_kwargs={"env_id": env_id, "run_name": run_name}) - if ALGORITHM == "sac": - model = SAC( - "MlpPolicy", - env, - tensorboard_log=f"runs/{run.id}", - learning_starts=gen_learning_start(env_id), - gamma=gen_gamma(env_id), - use_sde=False, - seed=seed, - replay_buffer_class=InfoReplayBuffer, - replay_buffer_kwargs={"info_keys": INFO_KEYS[env_id]}, - ) - elif ALGORITHM == "td3": - model = TD3( - "MlpPolicy", - env, - tensorboard_log=f"runs/{run.id}", - learning_starts=gen_learning_start(env_id), - gamma=gen_gamma(env_id), - seed=seed, - replay_buffer_class=InfoReplayBuffer, - replay_buffer_kwargs={"info_keys": INFO_KEYS[env_id]}, - ) - elif ALGORITHM == "ppo": - model = PPO( - "MlpPolicy", - env, - tensorboard_log=f"runs/{run.id}", - gamma=gen_gamma(env_id), - seed=seed, - ent_coef=5e-6, - device="cpu", - ) + model = initialize_mode(ALGORITHM) wandb_callback = WandbCallback() # Save a checkpoint every 100000 steps @@ -301,7 +303,6 @@ def make_env(env_id: str, run_name: str): save_freq=int(100000 / n_envs), save_path=f"logs/{env_id}/", name_prefix="rl_model", - # save_replay_buffer=True, save_replay_buffer=False, ) @@ -311,7 +312,10 @@ def make_env(env_id: str, run_name: str): print("FINISHED LEARNING") - eval_env = make_vec_env(f"{env_id}-v5", n_envs=1) + if env_id == "HumandoidStandup": + eval_env = make_vec_env(f"{env_id}-v5", n_envs=1, env_kwargs={"include_cinert_in_observation":False, "include_cvel_in_observation":False, "include_qfrc_actuator_in_observation":False, "include_cfrc_ext_in_observation":False}) + else: + eval_env = make_vec_env(f"{env_id}-v5", n_envs=1) # Replace InfoReplayBuffer for sb3 ReplayBuffer if ALGORITHM == "sac": @@ -336,8 +340,9 @@ def make_env(env_id: str, run_name: str): model_architecture=ALGORITHM.upper(), env_id=f"{env_id}-v5", eval_env=eval_env, - repo_id=f"farama-minari/{env_id}-v5-{ALGORITHM.upper()}-{PROFICIENCY}", + repo_id=f"{HF_REPO}/{env_id}-v5-{ALGORITHM.upper()}-{PROFICIENCY}", commit_message="model", ) print("FINISHED HF") +

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