Instructions to use umjunsik1323/RL_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use umjunsik1323/RL_Models with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="umjunsik1323/RL_Models", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Agenlus Model Hub π
Welcome to your Agenlus Reinforcement Learning repository! This repository hosts multiple trained models.
π Models Summary
| Model Name | Environment | Algorithm | Best Score | Episodes | Links |
|---|---|---|---|---|---|
| PPO-CartPole-v1-ep106 | CartPole-v1 |
PPO |
55.94 | 106 | Browse Files |
π Model Details & Instructions
π¦ PPO-CartPole-v1-ep106
- Environment:
CartPole-v1 - RL Algorithm:
PPO - Best Avg Reward:
55.94 - Episodes Trained:
106
Description: PPO model trained on CartPole-v1 for 106 episodes. Best avg reward: 55.94.
How to load:
const model = await tf.loadLayersModel('https://huggingface.co/umjunsik1323/RL_Models/raw/main/PPO-CartPole-v1-ep106/model.json');
- Downloads last month
- -