Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -163.04 +/- 104.28
+      value: -113.67 +/- 12.83
       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 0x7da252b9b880>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7da252b9b910>", "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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ppo-LunarLander-v2-2/_stable_baselines3_version

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ppo-LunarLander-v2-2/data

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ppo-LunarLander-v2-2/pytorch_variables.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2-2/system_info.txt

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+- OS: Linux-6.5.6-76060506-generic-x86_64-with-glibc2.35 # 202310061235~1697396945~22.04~9283e32 SMP PREEMPT_DYNAMIC Sun O
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.1.0
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.28.1

results.json

@@ -1 +1 @@
-{"mean_reward": -163.03661754149942, "std_reward": 104.2771307510438, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-25T03:06:24.975671"}
+{"mean_reward": -113.66928549999997, "std_reward": 12.825518606502003, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-25T13:37:19.107250"}