Upload best PPO LunarLander-v2 agent (tuned with Optuna).

PPO-LunarLander-v2-optuna-tuning.zip

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PPO-LunarLander-v2-optuna-tuning/_stable_baselines3_version

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PPO-LunarLander-v2-optuna-tuning/data

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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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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f36779e0a60>",
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+        "__abstractmethods__": "frozenset()",
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+        "bounded_above": "[False False False False False False False False]",
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PPO-LunarLander-v2-optuna-tuning/policy.optimizer.pth

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+oid sha256:705069b5bb97cb0ba2d8e9f04ddeb6e422ffd814835208c1c492c072a504cfc7
+size 87929

PPO-LunarLander-v2-optuna-tuning/policy.pth

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+oid sha256:99acca5e8f9f7f3c3dbc91a82f414b1399c14cd2822cb1c2c81423d6dff14048
+size 43393

PPO-LunarLander-v2-optuna-tuning/pytorch_variables.pth

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PPO-LunarLander-v2-optuna-tuning/system_info.txt

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+- OS: Linux-5.15.0-67-generic-x86_64-with-glibc2.29 # 74~20.04.1-Ubuntu SMP Wed Feb 22 14:52:34 UTC 2023
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu117
+- GPU Enabled: True
+- Numpy: 1.24.2
+- Gym: 0.21.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 119.96 +/- 120.32
+      value: 261.01 +/- 72.19
       name: mean_reward
       verified: false
 ---

config.json

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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 0x7fc080fa54c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc080fa5550>", "reset_noise": 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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results.json

@@ -1 +1 @@
-{"mean_reward": 119.96047343111745, "std_reward": 120.32348713766541, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-21T11:57:57.841700"}
+{"mean_reward": 261.01353898516845, "std_reward": 72.18503634432798, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-28T14:03:56.736450"}