Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 245.12 +/- 70.01
+      value: 287.42 +/- 19.54
       name: mean_reward
       verified: false
 ---
@@ -26,27 +26,12 @@ This is a trained model of a **PPO** agent playing **LunarLander-v2**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)
+TODO: Add your code
 
 
 ```python
-# pip install shimmy
-import gymnasium as gym
-
+from stable_baselines3 import ...
 from huggingface_sb3 import load_from_hub
 
-from stable_baselines3 import PPO
-from stable_baselines3.common.evaluation import evaluate_policy
-from stable_baselines3.common.monitor import Monitor
-
-repo_id = "AdanLee/ppo-LunarLander-v2"
-filename = "ppo-LunarLander-v2.zip"
-
-custom_objects = {
-    "learning_rate": 0.0,
-    "lr_schedule": lambda _: 0.0,
-    "clip_range": lambda _: 0.0,
-}
-
-checkpoint = load_from_hub(repo_id, filename)
-model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
+...
 ```

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 0x78ed006d0280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78ed006d0310>", "reset_noise": 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ppo-LunarLander-v2/policy.optimizer.pth

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ppo-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:a6c5104135d68809e61791d61c35638a72d03a1b9ea50d1138a86df908db742c
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 size 43329

ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.0.1+cu118
 - GPU Enabled: True
-- Numpy: 1.22.4
+- Numpy: 1.23.5
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
 - OpenAI Gym: 0.25.2

results.json

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