Initial commit
Browse files- README.md +37 -0
- a2c-PandaReachDense-v2_v5.zip +3 -0
- a2c-PandaReachDense-v2_v5/_stable_baselines3_version +1 -0
- a2c-PandaReachDense-v2_v5/data +94 -0
- a2c-PandaReachDense-v2_v5/policy.optimizer.pth +3 -0
- a2c-PandaReachDense-v2_v5/policy.pth +3 -0
- a2c-PandaReachDense-v2_v5/pytorch_variables.pth +3 -0
- a2c-PandaReachDense-v2_v5/system_info.txt +7 -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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- PandaReachDense-v2
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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: PandaReachDense-v2
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type: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -1.88 +/- 0.86
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name: mean_reward
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verified: false
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---
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# **A2C** Agent playing **PandaReachDense-v2**
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This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
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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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a2c-PandaReachDense-v2_v5.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:9c5d93ace80b86f3d23123176f7e206c3b71712d543b60ebcdac58e6232f0814
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size 108023
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a2c-PandaReachDense-v2_v5/_stable_baselines3_version
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1.7.0
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a2c-PandaReachDense-v2_v5/data
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{
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"policy_class": {
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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 0x7f63012cd8b0>",
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"__abstractmethods__": "frozenset()",
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