raj79 commited on
Commit
a1f62b5
·
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1 Parent(s): 2251aaf

Update batch size

Browse files
README.md CHANGED
@@ -1,37 +1,37 @@
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- ---
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- library_name: stable-baselines3
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- tags:
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- - LunarLander-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: PPO
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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: LunarLander-v2
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- type: LunarLander-v2
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- metrics:
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- - type: mean_reward
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- value: 240.07 +/- 46.24
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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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- # **PPO** Agent playing **LunarLander-v2**
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- This is a trained model of a **PPO** agent playing **LunarLander-v2**
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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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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - LunarLander-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: PPO
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+ results:
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+ - task:
12
+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: LunarLander-v2
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+ type: LunarLander-v2
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+ metrics:
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+ - type: mean_reward
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+ value: 279.31 +/- 15.70
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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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+ # **PPO** Agent playing **LunarLander-v2**
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+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
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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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+ ```
config.json CHANGED
@@ -1 +1 @@
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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). 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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7f488c73e170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f488c73e200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f488c73e290>", 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  },
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  }
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  }
 
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  "__module__": "stable_baselines3.common.policies",
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  "__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). 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: Features extractor to use.\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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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x0000022538321120>",
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+ "_build": "<function ActorCriticPolicy._build at 0x00000225383212D0>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x00000225383215A0>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x0000022538321630>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x00000225383216C0>",
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  "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x000002253813FE00>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {},
 
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  "action_noise": null,
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