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MattStammers/ppo-LunarLander-v2
ppo-LunarLander-v2 is a reinforcement learning model from MattStammers. 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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From the Hugging Face model README
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
First DL agent. Feel free to use for whatever lunar landings are required.
# To load it and watch it land (on your computer NOT collab! You have to ditch render-mode="human" to run it in a notebook without visuals)
import gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="MattStammers/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
model = PPO.load(checkpoint)
# Evaluate the agent and watch it land!
eval_env = gym.make('LunarLander-v2', render_mode="human")
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
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