Retrain PPO model for CarRacing-v0 v0

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
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+*.mp4 filter=lfs diff=lfs merge=lfs -text

PPO-CarRacing-v0.zip

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PPO-CarRacing-v0/_stable_baselines3_version

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+1.5.0

PPO-CarRacing-v0/policy.optimizer.pth

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PPO-CarRacing-v0/policy.pth

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PPO-CarRacing-v0/pytorch_variables.pth

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PPO-CarRacing-v0/system_info.txt

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+OS: Linux-5.10.107+-x86_64-with-debian-bullseye-sid #1 SMP Sun Apr 24 15:04:08 UTC 2022
+Python: 3.7.12
+Stable-Baselines3: 1.5.0
+PyTorch: 1.9.1
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

README.md

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+---
+library_name: stable-baselines3
+tags:
+- CarRacing-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 81.28 +/- 82.32
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CarRacing-v0
+      type: CarRacing-v0
+---
+
+  # **PPO** Agent playing **CarRacing-v0**
+  This is a trained model of a **PPO** agent playing **CarRacing-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  
+  ## Usage (with Stable-baselines3)
+  TODO: Add your code
+  

replay.mp4

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results.json

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+{"mean_reward": 81.27780058607459, "std_reward": 82.32042399214119, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-13T12:55:08.198971"}