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tqc-FetchPickAndPlace-v1-1014343346

Reinforcement Learning
stable-baselines3
FetchPickAndPlace-v1
deep-reinforcement-learning
Eval Results
Model card Files Community
Quentin Gallouédec commited on
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  1. .gitattributes +1 -0
  2. README.md +79 -0
  3. args.yml +83 -0
  4. config.yml +24 -0
  5. env_kwargs.yml +1 -0
  6. replay.mp4 +3 -0
  7. results.json +1 -0
  8. tqc-FetchPickAndPlace-v1.zip +3 -0
  9. tqc-FetchPickAndPlace-v1/_stable_baselines3_version +1 -0
  10. tqc-FetchPickAndPlace-v1/actor.optimizer.pth +3 -0
  11. tqc-FetchPickAndPlace-v1/critic.optimizer.pth +3 -0
  12. tqc-FetchPickAndPlace-v1/data +130 -0
  13. tqc-FetchPickAndPlace-v1/ent_coef_optimizer.pth +3 -0
  14. tqc-FetchPickAndPlace-v1/policy.pth +3 -0
  15. tqc-FetchPickAndPlace-v1/pytorch_variables.pth +3 -0
  16. tqc-FetchPickAndPlace-v1/system_info.txt +7 -0
  17. train_eval_metrics.zip +3 -0
.gitattributes CHANGED
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README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - FetchPickAndPlace-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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+ - 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: FetchPickAndPlace-v1
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+ type: FetchPickAndPlace-v1
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+ metrics:
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+ - type: mean_reward
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+ value: -8.40 +/- 3.44
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **TQC** Agent playing **FetchPickAndPlace-v1**
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+ This is a trained model of a **TQC** agent playing **FetchPickAndPlace-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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+ Install the RL Zoo (with SB3 and SB3-Contrib):
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+ ```bash
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+ pip install rl_zoo3
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+ ```
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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 rl_zoo3.load_from_hub --algo tqc --env FetchPickAndPlace-v1 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo tqc --env FetchPickAndPlace-v1 -f logs/
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+ ```
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+
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+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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+ ```
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+ python -m rl_zoo3.load_from_hub --algo tqc --env FetchPickAndPlace-v1 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo tqc --env FetchPickAndPlace-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 -m rl_zoo3.train --algo tqc --env FetchPickAndPlace-v1 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m rl_zoo3.push_to_hub --algo tqc --env FetchPickAndPlace-v1 -f logs/ -orga qgallouedec
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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', 2048),
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+ ('buffer_size', 1000000),
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+ ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
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+ ('gamma', 0.95),
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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, )'),
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+ ('tau', 0.05),
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+ ('normalize', False)])
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+ ```
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+ - - replay_buffer_class
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+ - HerReplayBuffer
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+ - - replay_buffer_kwargs
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+ - dict( online_sampling=True, goal_selection_strategy='future', n_sampled_goal=4,
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+ )
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+ - - tau
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+ - 0.05
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+ },
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+ "_n_updates": 999900,
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+ "buffer_size": 1,
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+ "batch_size": 2048,
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+ "learning_starts": 100,
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+ "tau": 0.05,
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+ "gamma": 0.95,
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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 0x7f1bce8f8ca0>",
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+ "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7f1bce8f8d30>",
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+ "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x7f1bce8f8dc0>",
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+ "set_env": "<function HerReplayBuffer.set_env at 0x7f1bce8f8e50>",
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+ "_get_samples": "<function HerReplayBuffer._get_samples at 0x7f1bce8f8ee0>",
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+ "sample": "<function HerReplayBuffer.sample at 0x7f1bce8f8f70>",
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+ "_sample_offline": "<function HerReplayBuffer._sample_offline at 0x7f1bce889040>",
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+ "sample_goals": "<function HerReplayBuffer.sample_goals at 0x7f1bce8890d0>",
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+ "_sample_transitions": "<function HerReplayBuffer._sample_transitions at 0x7f1bce889160>",
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+ "add": "<function HerReplayBuffer.add at 0x7f1bce8891f0>",
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+ "store_episode": "<function HerReplayBuffer.store_episode at 0x7f1bce889280>",
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+ "_sample_her_transitions": "<function HerReplayBuffer._sample_her_transitions at 0x7f1bce889310>",
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+ "n_episodes_stored": "<property object at 0x7f1bce885c70>",
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+ "size": "<function HerReplayBuffer.size at 0x7f1bce889430>",
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+ "reset": "<function HerReplayBuffer.reset at 0x7f1bce8894c0>",
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+ "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7f1bce889550>",
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+ "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc._abc_data object at 0x7f1bce8887c0>"
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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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+ },
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