Upload PPO LunarLander-v2 trained agent, used 1 mil more steps with more loose variance hyperparameter.

FinetunedPPO_5mil_steps_total.zip

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+version https://git-lfs.github.com/spec/v1
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+size 156907

FinetunedPPO_5mil_steps_total/_stable_baselines3_version

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+1.7.0

FinetunedPPO_5mil_steps_total/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 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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+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f46846ef520>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f46846ef5b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f46846ef640>",
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+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f46846ef7f0>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f46846ef9a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f46846efa30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f46846efac0>",
+        "__abstractmethods__": "frozenset()",
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+        "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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FinetunedPPO_5mil_steps_total/system_info.txt

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+- OS: Linux-6.2.2-arch1-g14-1-x86_64-with-glibc2.37 # 5 SMP PREEMPT_DYNAMIC Sat, 04 Mar 2023 20:30:14 +0000
+- Python: 3.10.9
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+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: 263.40 +/- 11.32
+      value: 286.02 +/- 16.23
       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 0x7f17edb0f520>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f17edb0f5b0>", "reset_noise": 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results.json

@@ -1 +1 @@
-{"mean_reward": 263.40052418073543, "std_reward": 11.318721552532649, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-12T15:05:40.966596"}
+{"mean_reward": 286.0182618528187, "std_reward": 16.23159898013778, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T10:49:57.212941"}