Initial commit
Browse files- README.md +37 -0
- SAC-PandaPickAndPlaceDense-v3.zip +3 -0
- SAC-PandaPickAndPlaceDense-v3/_stable_baselines3_version +1 -0
- SAC-PandaPickAndPlaceDense-v3/data +76 -0
- SAC-PandaPickAndPlaceDense-v3/policy.optimizer.pth +3 -0
- SAC-PandaPickAndPlaceDense-v3/policy.pth +3 -0
- SAC-PandaPickAndPlaceDense-v3/pytorch_variables.pth +3 -0
- SAC-PandaPickAndPlaceDense-v3/system_info.txt +9 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
README.md
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---
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library_name: stable-baselines3
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tags:
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- PandaPickAndPlaceDense-v3
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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: SAC
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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: PandaPickAndPlaceDense-v3
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type: PandaPickAndPlaceDense-v3
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metrics:
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- type: mean_reward
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value: -50.00 +/- 0.00
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name: mean_reward
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verified: false
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---
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# **SAC** Agent playing **PandaPickAndPlaceDense-v3**
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This is a trained model of a **SAC** agent playing **PandaPickAndPlaceDense-v3**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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SAC-PandaPickAndPlaceDense-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:7d761bf1073dbbe46888466f3daa65341b850caaab3b1e848c9ab1b2d7a98385
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size 63459
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SAC-PandaPickAndPlaceDense-v3/_stable_baselines3_version
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2.0.0a5
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SAC-PandaPickAndPlaceDense-v3/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
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"__module__": "stable_baselines3.common.policies",
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"__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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"__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x0000021C7CE91620>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x0000021C7CE8A580>"
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},
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"verbose": 1,
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"policy_kwargs": {
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":type:": "<class 'dict'>",
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":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
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"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
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"optimizer_kwargs": {
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"alpha": 0.99,
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"eps": 1e-05,
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"weight_decay": 0
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}
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},
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"num_timesteps": 0,
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"_total_timesteps": 0,
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"_num_timesteps_at_start": 0,
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"seed": null,
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"action_noise": null,
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"start_time": 0.0,
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"learning_rate": 0.0007,
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"tensorboard_log": null,
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"_last_obs": null,
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"_last_episode_starts": null,
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"_last_original_obs": null,
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"_episode_num": 0,
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"use_sde": false,
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"sde_sample_freq": -1,
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"_current_progress_remaining": 1.0,
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"_stats_window_size": 100,
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"ep_info_buffer": null,
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"ep_success_buffer": null,
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"_n_updates": 0,
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"n_steps": 5,
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"gamma": 0.99,
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"gae_lambda": 1.0,
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"ent_coef": 0.0,
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"vf_coef": 0.5,
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"max_grad_norm": 0.5,
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"normalize_advantage": false,
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"observation_space": {
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":type:": "<class 'gymnasium.spaces.dict.Dict'>",
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":serialized:": "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",
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"spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (19,), float32))])",
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"_shape": null,
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"dtype": null,
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"_np_random": null
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},
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"action_space": {
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":type:": "<class 'gymnasium.spaces.box.Box'>",
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":serialized:": "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",
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"dtype": "float32",
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"bounded_below": "[ True True True True]",
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"bounded_above": "[ True True True True]",
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"_shape": [
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4
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],
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"low": "[-1. -1. -1. -1.]",
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"high": "[1. 1. 1. 1.]",
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"low_repr": "-1.0",
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"high_repr": "1.0",
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"_np_random": null
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},
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"n_envs": 4,
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"lr_schedule": {
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":type:": "<class 'function'>",
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":serialized:": "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"
|
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}
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}
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SAC-PandaPickAndPlaceDense-v3/policy.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:85a58f985e74b7e8b878b9b106f1d0800e93d55310ffc3581d87a5279c2bf27e
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size 1120
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SAC-PandaPickAndPlaceDense-v3/policy.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:543a0d46953c32b0084286c54894ca9bd1889b0090fa0dafbfd0d8488e154a31
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size 53231
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SAC-PandaPickAndPlaceDense-v3/pytorch_variables.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:fb4dde0c1ad63b7740276006a06cc491b21b407ea6c889928c223ec77ddad79f
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size 864
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SAC-PandaPickAndPlaceDense-v3/system_info.txt
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- OS: Windows-10-10.0.22631-SP0 10.0.22631
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- Python: 3.11.9
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- Stable-Baselines3: 2.0.0a5
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- PyTorch: 2.2.0
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- GPU Enabled: False
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- Numpy: 1.24.3
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- Cloudpickle: 3.0.0
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- Gymnasium: 0.28.1
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- OpenAI Gym: 0.26.2
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config.json
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. 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