Here is my first DRL model

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README.md +37 -0
config.json +1 -0
ppo-LunarLander-v2.zip +3 -0
ppo-LunarLander-v2/_stable_baselines3_version +1 -0
ppo-LunarLander-v2/data +99 -0
ppo-LunarLander-v2/policy.optimizer.pth +3 -0
ppo-LunarLander-v2/policy.pth +3 -0
ppo-LunarLander-v2/pytorch_variables.pth +3 -0
ppo-LunarLander-v2/system_info.txt +9 -0
replay.mp4 +0 -0
results.json +1 -0

README.md ADDED Viewed

	@@ -0,0 +1,37 @@

+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: MlpPolicy
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 259.77 +/- 17.96
+      name: mean_reward
+      verified: false
+---
+# **MlpPolicy** Agent playing **LunarLander-v2**
+This is a trained model of a **MlpPolicy** 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 ADDED Viewed

	@@ -0,0 +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 0x7fda60ea63b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fda60ea6440>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fda60ea64d0>", 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+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+cu121
+- GPU Enabled: False
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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