A2c PandaReachDense v3 | Sweet Tea Studio
Resources / A2c PandaReachDense v3 A2c PandaReachDense v3 A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library. Usage (with Stable-baselines3) TODO: Add your code
Verified source
Kind reinforcement-learning Version v68c6b06ec90a0869ac4f296e844bd1bb9ee1daa3 Publisher @zhiliang1 C grade Model source
Kind reinforcement-learning
Version v68c6b06ec90a0869ac4f296e844bd1bb9ee1daa3
Source Hugging Face --- 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: -0.17 +/- 0.12 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 ... ```
Sources & provenance
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3 excerpts This is a trained model of a A2C agent playing PandaReachDense-v3
A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library. Usage (with Stable-baselines3) TODO: Add your code
zhiliang1/a2c-PandaReachDense-v3