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a2c-PandaPickAndPlace-v3

Reinforcement Learning
stable-baselines3
PandaPickAndPlace-v3
deep-reinforcement-learning
Eval Results (legacy)
Model card Files
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  1. README.md +37 -0
  2. a2c-PandaPickAndPlace-v3.zip +3 -0
  3. a2c-PandaPickAndPlace-v3/_stable_baselines3_version +1 -0
  4. a2c-PandaPickAndPlace-v3/data +97 -0
  5. a2c-PandaPickAndPlace-v3/policy.optimizer.pth +3 -0
  6. a2c-PandaPickAndPlace-v3/policy.pth +3 -0
  7. a2c-PandaPickAndPlace-v3/pytorch_variables.pth +3 -0
  8. a2c-PandaPickAndPlace-v3/system_info.txt +9 -0
  9. config.json +1 -0
  10. results.json +1 -0
  11. vec_normalize.pkl +3 -0
README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - PandaPickAndPlace-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: A2C
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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: PandaPickAndPlace-v3
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+ type: PandaPickAndPlace-v3
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+ metrics:
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+ - type: mean_reward
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+ value: -40.00 +/- 20.00
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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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+ # **A2C** Agent playing **PandaPickAndPlace-v3**
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+ This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+
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+ ## Usage (with Stable-baselines3)
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+ TODO: Add your code
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+
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+
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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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+ ```
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a2c-PandaPickAndPlace-v3/_stable_baselines3_version ADDED
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+ 2.1.0
a2c-PandaPickAndPlace-v3/data ADDED
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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 0x0000018F40BD9EA0>",
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+ "__abstractmethods__": "frozenset()",
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+ },
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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": 50560,
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+ "learning_rate": 0.0007,
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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 ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x0000018F40BD9EA0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x0000018F40BDCB40>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": 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