Test training for HF DRL course unit1

PPO-LunarLander-v2-10000.zip

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PPO-LunarLander-v2-10000/_stable_baselines3_version

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PPO-LunarLander-v2-10000/data

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+    "normalize_advantage": true,
+    "target_kl": null
+}

PPO-LunarLander-v2-10000/policy.optimizer.pth

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+size 87929

PPO-LunarLander-v2-10000/policy.pth

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+oid sha256:3bc71dec5f616b63f4f25542e144095d586cb24c70887028a50f8d16ea3740e7
+size 43201

PPO-LunarLander-v2-10000/pytorch_variables.pth

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+size 431

PPO-LunarLander-v2-10000/system_info.txt

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+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

README.md

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+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: -615.56 +/- 200.15
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+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 ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

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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 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 0x7fa174323af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa174323b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa174323c10>", "_build_mlp_extractor": "<function 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results.json

@@ -0,0 +1 @@
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