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a2c-PandaReachDense-v3

Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Model card Files
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Commit a2c-1000k-v2 model

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  1. README.md +4 -4
  2. a2c-1000k-v2-PandaReachDense-v3.zip +3 -0
  3. a2c-1000k-v2-PandaReachDense-v3/_stable_baselines3_version +1 -0
  4. a2c-1000k-v2-PandaReachDense-v3/data +112 -0
  5. a2c-1000k-v2-PandaReachDense-v3/policy.optimizer.pth +3 -0
  6. a2c-1000k-v2-PandaReachDense-v3/policy.pth +3 -0
  7. a2c-1000k-v2-PandaReachDense-v3/pytorch_variables.pth +3 -0
  8. a2c-1000k-v2-PandaReachDense-v3/system_info.txt +8 -0
  9. config.json +1 -1
  10. replay.mp4 +0 -0
  11. results.json +1 -1
  12. vec_normalize.pkl +2 -2
README.md CHANGED
@@ -6,7 +6,7 @@ tags:
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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
@@ -16,13 +16,13 @@ model-index:
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  type: PandaReachDense-v3
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  metrics:
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  - type: mean_reward
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- value: -0.21 +/- 0.14
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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-v3**
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- This is a trained model of a **A2C** agent playing **PandaReachDense-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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  - 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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  type: PandaReachDense-v3
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  metrics:
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  - type: mean_reward
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+ value: -0.16 +/- 0.09
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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-v3**
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+ This is a trained model of a **a2c** agent playing **PandaReachDense-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)
a2c-1000k-v2-PandaReachDense-v3.zip ADDED
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a2c-1000k-v2-PandaReachDense-v3/_stable_baselines3_version ADDED
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a2c-1000k-v2-PandaReachDense-v3/data ADDED
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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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