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Browse files- README.md +36 -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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- metrics:
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- type: mean_reward
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value: 65.27 +/- 147.53
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name: mean_reward
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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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---
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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 sha256:04feef31d9bd05a7f4240d228105847e7fd4d8a91096fb08d3ed0ee231f9a837
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size 26879741
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ppo-CarRacing-v0/_stable_baselines3_version
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1.6.0
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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:c7a0e9aa545982a1ff61056c5ea88f941a48be309c22de7ff253527fa0ee945f
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size 17415536
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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 sha256:9405b854d43b13f6a7b5f1a29aee04f90cd8d8c5734f1987a6346c5e62e34886
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size 8707070
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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: Linux-5.4.0-113-generic-x86_64-with-debian-buster-sid #127-Ubuntu SMP Wed May 18 14:30:56 UTC 2022
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Python: 3.7.13
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Stable-Baselines3: 1.6.0
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PyTorch: 1.12.0
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GPU Enabled: True
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Numpy: 1.21.5
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Gym: 0.21.0
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results.json
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{"mean_reward": 65.26687043309212, "std_reward": 147.53181521026872, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-22T19:27:48.314989"}
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