First sweep attempt

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 257.33 +/- 38.49
+      value: 288.77 +/- 17.49
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 0x7f53e6de0af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f53e6de0b80>", "reset_noise": 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ppo-LunarLander-v2-sweep.zip

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ppo-LunarLander-v2-sweep/_stable_baselines3_version

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ppo-LunarLander-v2-sweep/data

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ppo-LunarLander-v2-sweep/policy.optimizer.pth

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ppo-LunarLander-v2-sweep/pytorch_variables.pth

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ppo-LunarLander-v2-sweep/system_info.txt

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+- OS: Linux-5.4.0-166-generic-x86_64-with-glibc2.31 # 183-Ubuntu SMP Mon Oct 2 11:28:33 UTC 2023
+- Python: 3.10.13
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu121
+- GPU Enabled: True
+- Numpy: 1.26.1
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.28.1

results.json

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-{"mean_reward": 257.32790155685086, "std_reward": 38.48869333881314, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-02T16:59:30.152963"}
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