nbiish commited on
Commit
094b889
1 Parent(s): e34e8b8

The key is a while loop on the mean_reward variable when evaluating your agent🥰📚

Browse files
README.md CHANGED
@@ -16,7 +16,7 @@ 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: 283.22 +/- 14.84
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  name: mean_reward
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  verified: false
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  ---
 
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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+ value: 268.89 +/- 30.99
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  name: mean_reward
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  verified: false
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  ---
config.json CHANGED
@@ -1 +1 @@
1
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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 0x7fef12b74160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fef12b741f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fef12b74280>", 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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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- "__init__": "<function ActorCriticPolicy.__init__ at 0x7fef12b74160>",
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- "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fef12b741f0>",
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- "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fef12b74310>",
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- "_build": "<function ActorCriticPolicy._build at 0x7fef12b743a0>",
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- "forward": "<function ActorCriticPolicy.forward at 0x7fef12b74430>",
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- "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fef12b744c0>",
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- "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fef12b74550>",
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- "_predict": "<function ActorCriticPolicy._predict at 0x7fef12b745e0>",
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- "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fef12b74670>",
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- "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fef12b74700>",
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- "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fef12b74790>",
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  "__abstractmethods__": "frozenset()",
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- "_abc_impl": "<_abc._abc_data object at 0x7fef12b67300>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {},
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@@ -41,17 +41,17 @@
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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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+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fcb7d180e50>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcb7d181480>",
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  "__abstractmethods__": "frozenset()",
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  "verbose": 1,
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