Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use yumingyi/LunarLander with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use yumingyi/LunarLander with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="yumingyi/LunarLander", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Updated PPO LunarLander
Browse files- LunarLander3.zip +1 -1
- README.md +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
LunarLander3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 146767
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version https://git-lfs.github.com/spec/v1
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oid sha256:5f3d95c3c23a2a1fdfb381ae73da30f393247a431c99d913ea555767140e57d2
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size 146767
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README.md
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type: LunarLander-v2
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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-v2
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metrics:
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- type: mean_reward
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value: 278.25 +/- 16.02
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name: mean_reward
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verified: false
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---
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replay.mp4
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results.json
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{"mean_reward":
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{"mean_reward": 278.2515796498815, "std_reward": 16.017312283963378, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T12:44:40.970935"}
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