Reinforcement Learning
stable-baselines3
FrozenLake-v1
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use mindwrapped/ppo-FrozenLake-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use mindwrapped/ppo-FrozenLake-v1 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="mindwrapped/ppo-FrozenLake-v1", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
metadata
library_name: stable-baselines3
tags:
- FrozenLake-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 0.78 +/- 0.42
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
PPO Agent playing FrozenLake-v1
This is a trained model of a PPO agent playing FrozenLake-v1 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code