2m steps

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 267.37 +/- 23.07
+      value: 285.92 +/- 20.13
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -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 0x7f214ebadca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f214ebadd30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f214ebaddc0>", "_build_mlp_extractor": "<function 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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. 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ppo-LunarLander-v2.zip

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+size 144158

ppo-LunarLander-v2/data

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         "__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 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    ",
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-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f214ebadd30>",
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-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f214ebade50>",
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-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f214ebb21f0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f214ebb2280>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9026771dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9026771e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9026771ee0>",
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+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9026775040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f90267750d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9026775160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f90267751f0>",
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         "__abstractmethods__": "frozenset()",
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     "verbose": 1,
     "policy_kwargs": {},
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     "learning_rate": 0.0003,
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+    },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
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@@ -63,16 +66,16 @@
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     "use_sde": false,
     "sde_sample_freq": -1,
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+    "_current_progress_remaining": -0.0010623999999999079,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
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