Tried running the lunar lander PPO with 10mil timestamps; limited success.

LunarLander-v2-10m_ts.zip

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LunarLander-v2-10m_ts/_stable_baselines3_version

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+2.0.0a5

LunarLander-v2-10m_ts/data

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+}

LunarLander-v2-10m_ts/policy.optimizer.pth

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LunarLander-v2-10m_ts/policy.pth

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LunarLander-v2-10m_ts/pytorch_variables.pth

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LunarLander-v2-10m_ts/system_info.txt

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+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 259.75 +/- 12.46
+      value: 302.85 +/- 14.52
       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 0x7815ce6ac9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7815ce6aca60>", "reset_noise": 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results.json

@@ -1 +1 @@
-{"mean_reward": 259.7505985760466, "std_reward": 12.456677554269561, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-28T14:58:08.315476"}
+{"mean_reward": 302.84871481441974, "std_reward": 14.520752642652598, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-28T16:59:05.465546"}