Instructions to use Aadit-032/ppo-LunarLander-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Aadit-032/ppo-LunarLander-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Aadit-032/ppo-LunarLander-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from Aadit-032/ppo-LunarLander-v3: direct link, hf CLI and curl.
- Browser
- Download file 566 Bytes
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https://huggingface.co/Aadit-032/ppo-LunarLander-v3/resolve/main/README.md
- Command line
-
hf download hf://Aadit-032/ppo-LunarLander-v3/README.md
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curl -L -o README.md https://huggingface.co/Aadit-032/ppo-LunarLander-v3/resolve/main/README.md
566 Bytes
metadata
license: mit
library_name: stable-baselines3
tags:
- deep-reinforcement-learning
- gymnasium
- lunar-lander
- ppo
- sb3
PPO LunarLander-v3
This repository contains a Stable-Baselines3 PPO agent trained to solve the Gymnasium LunarLander environment.
Training setup
- Algorithm: PPO
- Environment: LunarLander-v3
- Policy: MlpPolicy
- Training timesteps: 1,000,000
Evaluation
The agent was evaluated on the LunarLander environment with deterministic rollout settings.
Notes
This model is intended for experimentation and educational purposes.