tobyych commited on
Commit
7f24ec8
1 Parent(s): abc850e

upload PPO LunarLander-v2 trained agent

Browse files

README.md CHANGED
@@ -10,7 +10,7 @@ model-index:
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  results:
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  - metrics:
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  - type: mean_reward
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- value: -16.50 +/- 57.79
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  name: mean_reward
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  task:
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  type: reinforcement-learning
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  results:
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  - metrics:
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  - type: mean_reward
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+ value: 254.64 +/- 22.65
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  name: mean_reward
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  task:
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  type: reinforcement-learning
config.json CHANGED
@@ -1 +1 @@
1
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If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7fa5e0ac75e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa5e0ac7670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa5e0ac7700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa5e0ac7790>", "_build": "<function 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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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7fa5e0ac75e0>",
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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa5e0ac7670>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa5e0ac7700>",
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+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa5e0ac7790>",
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+ "_build": "<function ActorCriticPolicy._build at 0x7fa5e0ac7820>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x7fa5e0ac78b0>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa5e0ac7940>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x7fa5e0ac79d0>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa5e0ac7a60>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa5e0ac7af0>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa5e0ac7b80>",
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  "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7fa5e0abda50>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {},
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  "observation_space": {
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  "dtype": "float32",
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+ "shape": [
 
 
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  8
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  ],
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  "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
31
  "high": "[inf inf inf inf inf inf inf inf]",
32
+ "bounded_below": "[False False False False False False False False]",
33
+ "bounded_above": "[False False False False False False False False]",
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  "_np_random": null
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  },
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  "action_space": {
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