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tqc-FetchPush-v1

Reinforcement Learning
stable-baselines3
FetchPush-v1
deep-reinforcement-learning
Eval Results
Model card Files Community
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  1. .gitattributes +1 -0
  2. README.md +67 -0
  3. args.yml +63 -0
  4. config.yml +24 -0
  5. env_kwargs.yml +1 -0
  6. replay.mp4 +3 -0
  7. results.json +1 -0
  8. tqc-FetchPush-v1.zip +3 -0
  9. tqc-FetchPush-v1/_stable_baselines3_version +1 -0
  10. tqc-FetchPush-v1/actor.optimizer.pth +3 -0
  11. tqc-FetchPush-v1/critic.optimizer.pth +3 -0
  12. tqc-FetchPush-v1/data +130 -0
  13. tqc-FetchPush-v1/ent_coef_optimizer.pth +3 -0
  14. tqc-FetchPush-v1/policy.pth +3 -0
  15. tqc-FetchPush-v1/pytorch_variables.pth +3 -0
  16. tqc-FetchPush-v1/system_info.txt +7 -0
  17. train_eval_metrics.zip +3 -0
.gitattributes CHANGED
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README.md ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - FetchPush-v1
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: TQC
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+ results:
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+ - metrics:
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+ - type: mean_reward
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+ value: -11.60 +/- 6.20
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+ name: mean_reward
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+ task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: FetchPush-v1
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+ type: FetchPush-v1
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+ ---
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+
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+ # **TQC** Agent playing **FetchPush-v1**
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+ This is a trained model of a **TQC** agent playing **FetchPush-v1**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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+
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+ The RL Zoo is a training framework for Stable Baselines3
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+ reinforcement learning agents,
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+ with hyperparameter optimization and pre-trained agents included.
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+
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+ ## Usage (with SB3 RL Zoo)
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+
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+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+
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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m utils.load_from_hub --algo tqc --env FetchPush-v1 -orga sb3 -f logs/
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+ python enjoy.py --algo tqc --env FetchPush-v1 -f logs/
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+ ```
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+
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+ ## Training (with the RL Zoo)
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+ ```
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+ python train.py --algo tqc --env FetchPush-v1 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m utils.push_to_hub --algo tqc --env FetchPush-v1 -f logs/ -orga sb3
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+ ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('batch_size', 512),
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+ ('buffer_size', 1000000),
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+ ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
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+ ('gamma', 0.98),
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+ ('learning_rate', 0.001),
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+ ('n_timesteps', 1000000.0),
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+ ('policy', 'MultiInputPolicy'),
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+ ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
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+ ('replay_buffer_class', 'HerReplayBuffer'),
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+ ('replay_buffer_kwargs',
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+ "dict( online_sampling=True, goal_selection_strategy='future', "
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+ 'n_sampled_goal=4, max_episode_length=100 )'),
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+ ('tau', 0.005),
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+ ('normalize', False)])
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+ ```
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+ - 50000
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+ - - gym_packages
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+ - HerReplayBuffer
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+ - - replay_buffer_kwargs
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+ max_episode_length=100 )
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+ - - tau
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+ "_n_updates": 999900,
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+ "buffer_size": 1,
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+ "batch_size": 512,
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+ "learning_starts": 100,
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+ "tau": 0.005,
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+ "gamma": 0.98,
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+ "gradient_steps": 1,
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+ "optimize_memory_usage": false,
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+ "replay_buffer_class": {
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+ ":type:": "<class 'abc.ABCMeta'>",
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+ ":serialized:": "gAWVPwAAAAAAAACMJ3N0YWJsZV9iYXNlbGluZXMzLmhlci5oZXJfcmVwbGF5X2J1ZmZlcpSMD0hlclJlcGxheUJ1ZmZlcpSTlC4=",
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+ "__module__": "stable_baselines3.her.her_replay_buffer",
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+ "__doc__": "\n Hindsight Experience Replay (HER) buffer.\n Paper: https://arxiv.org/abs/1707.01495\n\n .. warning::\n\n For performance reasons, the maximum number of steps per episodes must be specified.\n In most cases, it will be inferred if you specify ``max_episode_steps`` when registering the environment\n or if you use a ``gym.wrappers.TimeLimit`` (and ``env.spec`` is not None).\n Otherwise, you can directly pass ``max_episode_length`` to the replay buffer constructor.\n\n\n Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n In the online sampling case, these new transitions will not be saved in the replay buffer\n and will only be created at sampling time.\n\n :param env: The training environment\n :param buffer_size: The size of the buffer measured in transitions.\n :param max_episode_length: The maximum length of an episode. If not specified,\n it will be automatically inferred if the environment uses a ``gym.wrappers.TimeLimit`` wrapper.\n :param goal_selection_strategy: Strategy for sampling goals for replay.\n One of ['episode', 'final', 'future']\n :param device: PyTorch device\n :param n_sampled_goal: Number of virtual transitions to create per real transition,\n by sampling new goals.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
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+ "__init__": "<function HerReplayBuffer.__init__ at 0x7fbf70856b90>",
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+ "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7fbf70856c20>",
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+ "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x7fbf70856cb0>",
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+ "set_env": "<function HerReplayBuffer.set_env at 0x7fbf70856d40>",
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+ "_get_samples": "<function HerReplayBuffer._get_samples at 0x7fbf70856dd0>",
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+ "sample": "<function HerReplayBuffer.sample at 0x7fbf70856e60>",
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+ "_sample_offline": "<function HerReplayBuffer._sample_offline at 0x7fbf70856ef0>",
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+ "sample_goals": "<function HerReplayBuffer.sample_goals at 0x7fbf70856f80>",
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+ "_sample_transitions": "<function HerReplayBuffer._sample_transitions at 0x7fbf703cd050>",
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+ "add": "<function HerReplayBuffer.add at 0x7fbf703cd0e0>",
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+ "store_episode": "<function HerReplayBuffer.store_episode at 0x7fbf703cd170>",
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+ "_sample_her_transitions": "<function HerReplayBuffer._sample_her_transitions at 0x7fbf703cd200>",
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+ "n_episodes_stored": "<property object at 0x7fbf708593b0>",
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+ "size": "<function HerReplayBuffer.size at 0x7fbf703cd320>",
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+ "reset": "<function HerReplayBuffer.reset at 0x7fbf703cd3b0>",
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+ "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7fbf703cd440>",
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+ "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc_data object at 0x7fbf70857210>"
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+ },
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+ "replay_buffer_kwargs": {
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+ "online_sampling": true,
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+ "goal_selection_strategy": "future",
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+ "n_sampled_goal": 4,
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+ "max_episode_length": 100
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+ },
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+ "train_freq": {
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