Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 207.89 +/- 90.71
+      value: 251.61 +/- 27.88
       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 0x7c1f4a3ce830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c1f4a3ce8c0>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/data

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         "__module__": "stable_baselines3.common.policies",
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     "max_grad_norm": 0.5,
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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/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
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.1.0+cu118
+- PyTorch: 2.1.0+cu121
 - GPU Enabled: True
 - Numpy: 1.23.5
 - Cloudpickle: 2.2.1

results.json

@@ -1 +1 @@
-{"mean_reward": 207.8915745200649, "std_reward": 90.70954818822177, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-25T05:36:29.992669"}
+{"mean_reward": 251.60564947065967, "std_reward": 27.882596852506687, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-27T01:02:50.790390"}