Upload PPO LunarLander-v2 trained agent

LunarLadner_PPO_agent.zip

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LunarLadner_PPO_agent/_stable_baselines3_version

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+1.6.0

LunarLadner_PPO_agent/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 0x7f58f55c4b00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f58f55c4b90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f58f55c4c20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f58f55c4cb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f58f55c4d40>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f58f55c4ef0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f58f55c4f80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f58f55c9050>",
+        "__abstractmethods__": "frozenset()",
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+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
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+}

LunarLadner_PPO_agent/policy.optimizer.pth

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+size 87865

LunarLadner_PPO_agent/policy.pth

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+size 43201

LunarLadner_PPO_agent/pytorch_variables.pth

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LunarLadner_PPO_agent/system_info.txt

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+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 226.00 +/- 40.45
+      value: 122.13 +/- 88.65
       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 0x7f01ee182290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f01ee182320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f01ee1823b0>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
-{"mean_reward": 226.00109233617985, "std_reward": 40.454975214808016, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-05T16:30:56.844294"}
+{"mean_reward": 122.13008195096181, "std_reward": 88.65062346560445, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-08T15:01:56.077376"}