PPO LunarLander-v2 trained agent (upd hypers)

PPO_LunarLander-v2.zip

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 version https://git-lfs.github.com/spec/v1
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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    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7efb146cb320>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efb146cb3b0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efb146cb440>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efb146cb4d0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7efb146cb560>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7efb146cb5f0>",
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-        "_predict": "<function ActorCriticPolicy._predict at 0x7efb146cb710>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efb146cb7a0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efb146cb830>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efb146cb8c0>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe2a20617a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe2a2061830>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe2a20618c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe2a2061950>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe2a20619e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe2a2061a70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe2a2061b00>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe2a2061b90>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe2a2061c20>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7efb14770180>"
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     },
     "verbose": 1,
     "policy_kwargs": {},
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     "n_envs": 16,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
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README.md

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   results:
   - metrics:
     - type: mean_reward
-      value: -160.12 +/- 44.18
+      value: 215.13 +/- 40.65
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

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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 0x7efb146cb320>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efb146cb3b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efb146cb440>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
-{"mean_reward": -160.11789308850422, "std_reward": 44.18011828028374, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-07T19:32:49.068048"}
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