Reinforcement Learning
stable-baselines3
LunarLander-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use hellosara/ppo-LunarLander-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use hellosara/ppo-LunarLander-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="hellosara/ppo-LunarLander-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
File size: 585 Bytes
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library_name: stable-baselines3
tags:
- LunarLander-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v3
type: LunarLander-v3
metrics:
- type: mean_reward
value: 262.16 +/- 22.42
name: mean_reward
verified: false
# This line tells Hugging Face to show your video on the front page
video: https://huggingface.co/hellosara/ppo-LunarLander-v3/resolve/main/replay.mp4
---
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