Instructions to use marvameraj/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use marvameraj/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="marvameraj/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
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Download README.md from marvameraj/ppo-LunarLander-v2: direct link, hf CLI and curl.
- Browser
- Download file 636 Bytes
-
https://huggingface.co/marvameraj/ppo-LunarLander-v2/resolve/main/README.md
- Command line
-
hf download hf://marvameraj/ppo-LunarLander-v2/README.md
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curl -L -o README.md https://huggingface.co/marvameraj/ppo-LunarLander-v2/resolve/main/README.md
636 Bytes
metadata
tags:
- reinforcement-learning
- stable-baselines3
- LunarLander-v2
- deep-rl-course
model-index:
- name: ppo-LunarLander-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.79 +/- 20.99
name: mean_reward
PPO LunarLander-v2
Deep Reinforcement Learning Course — Unit 1.
Evaluation
Environment: LunarLander-v2
Mean reward: 269.79
Standard deviation: 20.99
Course score: 248.80
Evaluation episodes: 20
Training timesteps: 1,000,000