Upload PPO CarRacing-v0 trained agent 100.000 timesteps

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:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CarRacing-v0
+      type: CarRacing-v0
+    metrics:
+    - type: mean_reward
+      value: 206.22 +/- 147.81
+      name: mean_reward
+      verified: false
+---
+
+# **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
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

ppo-CarRacing-v0.zip

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+size 26882408

ppo-CarRacing-v0/_stable_baselines3_version

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+1.7.0

ppo-CarRacing-v0/policy.optimizer.pth

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+size 17415600

ppo-CarRacing-v0/policy.pth

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+size 8709950

ppo-CarRacing-v0/pytorch_variables.pth

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+size 431

ppo-CarRacing-v0/system_info.txt

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+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

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+{"mean_reward": 206.22155792191626, "std_reward": 147.80872170023997, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-16T15:43:55.965337"}