Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Eslam25/LunarLander-v2-PPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Eslam25/LunarLander-v2-PPO with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Eslam25/LunarLander-v2-PPO", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
To Import and Use the model
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
repo_id = "Eslam25/LunarLander-v2-PPO"
filename = "ppo_1st.zip"
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
- Downloads last month
- 5
Evaluation results
- mean_reward on LunarLander-v2self-reported265.04 +/- 38.0587