Reinforcement Learning
stable-baselines3
LunarLander-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use mjishu/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use mjishu/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="mjishu/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
first run of lunar lander
Browse files- PPO-Lunar-Lander-v3.zip +1 -1
- README.md +1 -1
- results.json +1 -1
PPO-Lunar-Lander-v3.zip
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version https://git-lfs.github.com/spec/v1
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size 150576
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version https://git-lfs.github.com/spec/v1
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oid sha256:240e6838222d1e8ad8ff87bdc6530873915c79f7d088783d6978ce5cbbd1fc3e
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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: 267.29 +/- 18.04
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name: mean_reward
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verified: false
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---
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results.json
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{"mean_reward":
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{"mean_reward": 267.29090719999994, "std_reward": 18.041518417422353, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-01-28T17:51:43.480538"}
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