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Pyro-X2
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PandaReach-A2C

Reinforcement Learning
stable-baselines3
PandaReach-v3
deep-reinforcement-learning
Eval Results
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  1. README.md +37 -0
  2. a2c_panda_reach.zip +3 -0
  3. a2c_panda_reach/_stable_baselines3_version +1 -0
  4. a2c_panda_reach/data +106 -0
  5. a2c_panda_reach/policy.optimizer.pth +3 -0
  6. a2c_panda_reach/policy.pth +3 -0
  7. a2c_panda_reach/pytorch_variables.pth +3 -0
  8. a2c_panda_reach/system_info.txt +8 -0
  9. config.json +1 -0
  10. results.json +1 -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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+ - PandaReach-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: PandaReach-v3
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+ type: PandaReach-v3
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+ metrics:
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+ - type: mean_reward
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+ value: -2.00 +/- 0.77
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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 **PandaReach-v3**
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+ This is a trained model of a **A2C** agent playing **PandaReach-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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+ ```
a2c_panda_reach.zip ADDED
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a2c_panda_reach/_stable_baselines3_version ADDED
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+ 2.4.0
a2c_panda_reach/data ADDED
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+ {
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+ "policy_class": {
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+ ":type:": "<class 'abc.ABCMeta'>",
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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 0x7711d875e8c0>",
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+ "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc._abc_data object at 0x7711d8757640>"
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+ },
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+ "verbose": 1,
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+ "policy_kwargs": {
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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": 2000000,
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+ "_total_timesteps": 2000000,
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+ "seed": null,
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+ "learning_rate": 0.001,
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log probability of each taken action.\n\n The term rollout here refers to the model-free notion and should not\n be used with the concept of rollout used in model-based RL or planning.\n Hence, it is only involved in policy and value function training but not action selection.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n Equivalent to Monte-Carlo advantage estimate when set to 1.\n :param gamma: Discount factor\n :param n_envs: Number of parallel environments\n ", "__init__": "<function DictRolloutBuffer.__init__ at 0x7711d8708550>", "reset": "<function DictRolloutBuffer.reset at 0x7711d87085e0>", "add": "<function DictRolloutBuffer.add at 0x7711d8708670>", "get": "<function DictRolloutBuffer.get at 0x7711d8708700>", "_get_samples": "<function DictRolloutBuffer._get_samples at 0x7711d8708790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7712646ffe00>"}, "rollout_buffer_kwargs": {}, "normalize_advantage": false, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": 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"system_info": {"OS": "Linux-6.8.0-49-generic-x86_64-with-glibc2.35 # 49~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Nov 6 17:42:15 UTC 2", "Python": "3.10.12", "Stable-Baselines3": "2.4.0", "PyTorch": "2.5.1+cu124", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "1.0.0"}}
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": -2.0, "std_reward": 0.7745966692414834, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-12-29T22:58:27.711207"}