Reinforcement Learning
stable-baselines3
LunarLander-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use hemanth2403/lunarlander with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use hemanth2403/lunarlander with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="hemanth2403/lunarlander", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
metadata
library_name: stable-baselines3
tags:
- LunarLander-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO and multi layer perceptron
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v3
type: LunarLander-v3
metrics:
- type: mean_reward
value: 266.89 +/- 22.05
name: mean_reward
verified: false
PPO and multi layer perceptron Agent playing LunarLander-v3
This is a trained model of a PPO and multi layer perceptron agent playing LunarLander-v3 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...