You can deploy this model and start earning money today!

karthikk1006/
ppo-LunarLander-v2

Name: ppo-LunarLander-v2
Availability: InStock

stable-baselines3

LunarLander-v2

deep-reinforcement-learning

reinforcement-learning

model-index

Views

No views yet

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

python

# !pip gymnasium huggingface-sb3 stable_baselines3[extra]
import gymnasium as gym
from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor

repo_id = "karthikk1006/ppo-LunarLander-v2"
filename = "ppo-LunarLander-v2.zip"
eval_env = gym.make("LunarLander-v2", render_mode="human")

checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint,print_system_info=True)

mean_reward, std_reward = evaluate_policy(model,eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# Enjoy trained agent
observation, info = eval_env.reset()
for _ in range(1000):
    action, _states = model.predict(observation, deterministic=True)
    observation, rewards, terminated, truncated, info = eval_env.step(action)
    eval_env.render()

...