trained ppo agent of lunar lander

README.md

@@ -0,0 +1,38 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO with MlpPolicy setting - verbose=1, n_steps=1024, batch_size=64, n_epochs
+    = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 271.13 +/- 22.25
+      name: mean_reward
+      verified: false
+---
+
+# **PPO with MlpPolicy setting - verbose=1, n_steps=1024, batch_size=64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO with MlpPolicy setting - verbose=1, n_steps=1024, batch_size=64, n_epochs = 4, gamma = 0.999, gae_lambda = 0.98, ent_coef = 0.01** 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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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 0x7a1f11b1fac0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a1f11b1fb50>", "reset_noise": 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ppo-LunarLander-v2_2/_stable_baselines3_version

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ppo-LunarLander-v2_2/data

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+}

ppo-LunarLander-v2_2/policy.optimizer.pth

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+size 87929

ppo-LunarLander-v2_2/policy.pth

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+size 43329

ppo-LunarLander-v2_2/pytorch_variables.pth

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ppo-LunarLander-v2_2/system_info.txt

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+- 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: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

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+{"mean_reward": 271.12609080000004, "std_reward": 22.248306116588527, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-27T16:40:06.944103"}