First Push
Browse files- README.md +6 -6
- a2c-v1.zip +2 -2
- a2c-v1/data +38 -50
- a2c-v1/policy.optimizer.pth +2 -2
- a2c-v1/policy.pth +2 -2
- a2c-v1/system_info.txt +1 -1
- config.json +1 -1
- replay.mp4 +2 -2
- results.json +1 -1
- vec_normalize.pkl +2 -2
README.md
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---
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library_name: stable-baselines3
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tags:
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-
-
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name:
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type:
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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# **A2C** Agent playing **
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This is a trained model of a **A2C** agent playing **
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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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---
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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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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: -0.80 +/- 0.27
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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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a2c-v1.zip
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n 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\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). 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