Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 266.12 +/- 20.19
+      value: 277.82 +/- 18.73
       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
-from stable_baselines3 import PPO
-from huggingface_sb3 import load_from_hub
 
+```python
+from stable_baselines3 import ...
 from huggingface_sb3 import load_from_hub
 
-repo_id = "Flooow/ppo-LunarLander-v2" # The repo_id
-filename = "ppo-LunarLander-v2.zip" # The model filename.zip
-
-# When the model was trained on Python 3.8 the pickle protocol is 5
-# But Python 3.6, 3.7 use protocol 4
-# In order to get compatibility we need to:
-# 1. Install pickle5 (we done it at the beginning of the colab)
-# 2. Create a custom empty object we pass as parameter to PPO.load()
-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 0x7f8ce9b7d550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8ce9b7d5e0>", "reset_noise": 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