5 million training steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 272.71 +/- 15.45
+      value: 282.27 +/- 17.43
       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 0x7f2799ff5c60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2799ff5cf0>", "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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mlp_model_5Msteps/_stable_baselines3_version

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mlp_model_5Msteps/pytorch_variables.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

mlp_model_5Msteps/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 272.7108853655076, "std_reward": 15.447135152468112, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-28T10:03:53.189364"}
+{"mean_reward": 282.27451668055664, "std_reward": 17.431081831576428, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-28T12:00:38.052604"}