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Upload PPO CarRacing-v0 trained agent
Browse files- README.md +37 -0
- config.json +0 -0
- ppo-CarRacing-v0.zip +3 -0
- ppo-CarRacing-v0/_stable_baselines3_version +1 -0
- ppo-CarRacing-v0/data +0 -0
- ppo-CarRacing-v0/policy.optimizer.pth +3 -0
- ppo-CarRacing-v0/policy.pth +3 -0
- ppo-CarRacing-v0/pytorch_variables.pth +3 -0
- ppo-CarRacing-v0/system_info.txt +7 -0
- results.json +1 -0
README.md
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---
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library_name: stable-baselines3
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tags:
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- CarRacing-v0
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: PPO
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: CarRacing-v0
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type: CarRacing-v0
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metrics:
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- type: mean_reward
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value: -63.85 +/- 2.86
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **CarRacing-v0**
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This is a trained model of a **PPO** agent playing **CarRacing-v0**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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config.json
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ppo-CarRacing-v0.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:ee49967a87f59cf674b48cdf4c9fceeac241506f38050d05c8490285f9d21710
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size 43336998
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ppo-CarRacing-v0/_stable_baselines3_version
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1.8.0
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ppo-CarRacing-v0/data
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ppo-CarRacing-v0/policy.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:52978444b846112a71f37722265b84807020b17be77c3222829c6e6483bee859
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size 28391152
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ppo-CarRacing-v0/policy.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:4fafc6ac2874f61d9a0c4aad2831e12ef4ada8afd6fd67f2cdea345273772ad2
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size 14194942
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ppo-CarRacing-v0/pytorch_variables.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
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size 431
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ppo-CarRacing-v0/system_info.txt
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- OS: Windows-10-10.0.22621-SP0 10.0.22621
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- Python: 3.10.11
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- Stable-Baselines3: 1.8.0
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- PyTorch: 2.0.0+cpu
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- GPU Enabled: False
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- Numpy: 1.24.1
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- Gym: 0.21.0
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results.json
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{"mean_reward": -63.84656130671501, "std_reward": 2.860821205018601, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-01T23:15:22.841791"}
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