Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use maskjp/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use maskjp/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="maskjp/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from maskjp/ppo-LunarLander-v2: direct link, hf CLI and curl.
- Browser
- Download file 784 Bytes
-
https://huggingface.co/maskjp/ppo-LunarLander-v2/resolve/main/README.md
- Command line
-
hf download hf://maskjp/ppo-LunarLander-v2/README.md
-
curl -L -o README.md https://huggingface.co/maskjp/ppo-LunarLander-v2/resolve/main/README.md
784 Bytes
metadata
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.27 +/- 18.45
name: mean_reward
verified: false
ppo Agent playing LunarLander-v2
This is a trained model of a ppo agent playing LunarLander-v2 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...