Reinforcement Learning
stable-baselines3
LunarLander-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use nnaxor/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use nnaxor/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="nnaxor/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- ppo-LunarLander-v2.zip +1 -1
- results.json +1 -1
README.md
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type: LunarLander-v3
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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type: LunarLander-v3
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metrics:
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- type: mean_reward
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value: 245.04 +/- 29.77
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name: mean_reward
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verified: false
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---
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ppo-LunarLander-v2.zip
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version https://git-lfs.github.com/spec/v1
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size 152590
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version https://git-lfs.github.com/spec/v1
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oid sha256:b8398f6b88ce32fd7c8ef286c6c2ab7b5fbae35a6f5eaf7221fae8e716916526
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size 152590
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results.json
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{"mean_reward":
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{"mean_reward": 245.04040283527848, "std_reward": 29.772422302657365, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-09-04T22:39:16.168282"}
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