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DQN-CartPole-v1.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2276fad8f6fbd86e2a38762bc314105bb061defc55f6a153c9bef8e01617d957
+size 558036

DQN-CartPole-v1/_stable_baselines3_version

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+2.1.0

DQN-CartPole-v1/data

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+        "__doc__": "\n    Policy class with Q-Value Net and target net for DQN\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\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    ",
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+    }
+}

DQN-CartPole-v1/policy.optimizer.pth

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+size 687

DQN-CartPole-v1/policy.pth

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DQN-CartPole-v1/pytorch_variables.pth

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DQN-CartPole-v1/system_info.txt

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+- OS: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Fri Jan 27 02:56:13 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.0.1+cu117
+- GPU Enabled: False
+- Numpy: 1.25.1
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.26.2

README.md

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+---
+library_name: stable-baselines3
+tags:
+- CartPole-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CartPole-v1
+      type: CartPole-v1
+    metrics:
+    - type: mean_reward
+      value: 10.60 +/- 0.80
+      name: mean_reward
+      verified: false
+---
+
+# **DQN** Agent playing **CartPole-v1**
+This is a trained model of a **DQN** agent playing **CartPole-v1**
+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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results.json

@@ -0,0 +1 @@
+{"mean_reward": 10.6, "std_reward": 0.7999999999999999, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-25T15:01:14.316186"}