SB3 PPO. Vectorized 16 env. ~ 9_000_000 timesteps of training. mean_reward=163 +/- 103 . Training for an additional 50_000_000 timesteps resulted in a worse reward when evaluating

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Pixelcopter-PLE-v0_4/policy.optimizer.pth

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Pixelcopter-PLE-v0_4/pytorch_variables.pth

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Pixelcopter-PLE-v0_4/system_info.txt

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+OS: Linux-5.4.0-136-generic-x86_64-with-glibc2.17 #153-Ubuntu SMP Thu Nov 24 15:56:58 UTC 2022
+Python: 3.8.15
+Stable-Baselines3: 1.7.0a10
+PyTorch: 1.13.1
+GPU Enabled: True
+Numpy: 1.23.5
+Gym: 0.21.0

README.md

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+---
+library_name: stable-baselines3
+tags:
+- Pixelcopter-PLE-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: ppo
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Pixelcopter-PLE-v0
+      type: Pixelcopter-PLE-v0
+    metrics:
+    - type: mean_reward
+      value: 162.90 +/- 102.90
+      name: mean_reward
+      verified: false
+---
+
+# **ppo** Agent playing **Pixelcopter-PLE-v0**
+This is a trained model of a **ppo** agent playing **Pixelcopter-PLE-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
+
+...
+```

config.json

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It allows to keep variance\n        above zero and prevent it from growing too fast. 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results.json

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+{"mean_reward": 162.9, "std_reward": 102.90038872618508, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-13T08:25:01.744757"}