Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use while0628/LunarLander-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use while0628/LunarLander-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="while0628/LunarLander-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Upload trained PPO agent for LunarLander-v3
Browse files- README.md +2 -2
- ppo-LunarLander-v3.zip +1 -1
- results.json +1 -1
README.md
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---
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library_name: stable-baselines3
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tags:
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- LunarLander-
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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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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---
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library_name: stable-baselines3
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tags:
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- LunarLander-v3
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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type: LunarLander-v3
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metrics:
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- type: mean_reward
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value: 229.75 +/- 84.38
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name: mean_reward
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verified: false
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---
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ppo-LunarLander-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 151138
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version https://git-lfs.github.com/spec/v1
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oid sha256:dee8672939b4b30cb21cac307952670379084dcc5ca7604e129c30df71060eb3
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size 151138
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results.json
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{"mean_reward":
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{"mean_reward": 229.75116010000002, "std_reward": 84.38220024189287, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2026-10-27T15:17:24.023861"}
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