Initial commit

A2C-PandaReachDense-v3.zip

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A2C-PandaReachDense-v3/_stable_baselines3_version

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A2C-PandaReachDense-v3/data

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+    }
+}

A2C-PandaReachDense-v3/policy.optimizer.pth

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A2C-PandaReachDense-v3/policy.pth

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A2C-PandaReachDense-v3/pytorch_variables.pth

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A2C-PandaReachDense-v3/system_info.txt

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+- OS: Linux-6.6.56+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Nov 10 10:07:59 UTC 2024
+- Python: 3.10.14
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.4.0
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.0
+- OpenAI Gym: 0.26.2

README.md

@@ -1,3 +1,37 @@
----
-license: mit
----
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.22 +/- 0.11
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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

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vec_normalize.pkl

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