Reinforcement Learning
stable-baselines3
MountainCar-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use FumaNet/TEST1PPO-MountainCar-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use FumaNet/TEST1PPO-MountainCar-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="FumaNet/TEST1PPO-MountainCar-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from FumaNet/TEST1PPO-MountainCar-v0: direct link, hf CLI and curl.
- Browser
- Download file 677 Bytes
-
https://huggingface.co/FumaNet/TEST1PPO-MountainCar-v0/resolve/main/README.md
- Command line
-
hf download hf://FumaNet/TEST1PPO-MountainCar-v0/README.md
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curl -L -o README.md https://huggingface.co/FumaNet/TEST1PPO-MountainCar-v0/resolve/main/README.md
677 Bytes
metadata
library_name: stable-baselines3
tags:
- MountainCar-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: '-200.00 +/- 0.00'
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MountainCar-v0
type: MountainCar-v0
PPO Agent playing MountainCar-v0
This is a trained model of a PPO agent playing MountainCar-v0 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code