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 huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy

# Download checkpoint
model_file = load_from_hub("umersheikh846/ppo-LunarLander-v2", "ppo-LunarLander-v2.zip")
# Load the model
model = PPO.load(model_file)

env = make_vec_env("LunarLander-v2", n_envs=1) # for training, 16 n_envs have been used, while 1 enough

# Evaluate the trained model with evaluate policy from SB3
print("Evaluating model")
mean_reward, std_reward = evaluate_policy(model,env, n_eval_episodes=10, deterministic=True)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")

# Start a new episode
obs = env.reset()

while True:
      action, _states = model.predict(obs, deterministic=True)
      obs, rewards, dones, info = env.step(action)
      env.render()


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