octipuw commited on
Commit
455a23d
1 Parent(s): afcf188

My first RL project!

Browse files
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: PP)
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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: LunarLander-v2
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  metrics:
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  - type: mean_reward
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- value: 233.72 +/- 51.44
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  name: mean_reward
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  verified: false
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  ---
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- # **PP)** Agent playing **LunarLander-v2**
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- This is a trained model of a **PP)** agent playing **LunarLander-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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  - 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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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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+ value: 111.00 +/- 84.65
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  name: mean_reward
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  verified: false
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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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  ## Usage (with Stable-baselines3)
config.json CHANGED
@@ -1 +1 @@
1
- {"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 0x7f12f67580d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f12f6758160>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f12f67581f0>", 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1
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It allows to keep variance\n above zero and prevent it from growing too fast. 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91
  },
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  "target_kl": null,
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  "lr_schedule": {
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98
  }
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  }
 
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  "__module__": "stable_baselines3.common.policies",
6
  "__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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7f939c95ff40>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f939c96c040>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f939c96c0d0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f939c96c160>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f939c96c1f0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f939c96c280>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f939c96c310>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f939c96c3a0>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f939c96c430>",
16
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