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ppo-lunarLander
ashna022/ppo-lunarLander
ppo-lunarLander is a reinforcement learning model from ashna022. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
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At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Jul 7, 2023
- Updated
- Jul 7, 2023
- SHA
- 7ea7e0c5
Model card
From the Hugging Face model README
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)
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from huggingface_sb3 import load_from_hub
# Get model
repo_id = "ashna022/ppo-lunarLander"
filename = "ppo-lunarLander"
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
# Evaluate
eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Jul 7, 2023
- Updated
- Jul 7, 2023