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)

import gymnasium as gym
from pathlib import Path

from huggingface_sb3 import load_from_hub, package_to_hub
from huggingface_hub import (
    notebook_login,
)  # To log to our Hugging Face account to be able to upload models to the Hub.

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor

from huggingface_sb3 import package_to_hub

# Create environment
env = gym.make("LunarLander-v2")

# Instantiate the agent
# We added some parameters to accelerate the training
model = PPO(
    policy="MlpPolicy",
    env=env,
    n_steps=1024,
    batch_size=64,
    n_epochs=4,
    gamma=0.999,
    gae_lambda=0.98,
    ent_coef=0.01,
    verbose=1,
)

# Train the agent
model_name = Path("ppo-LunarLander-v2.zip")
if not model_name.exists():
    model.learn(total_timesteps=1_000_000, progress_bar=True)
    # Remove the .zip adds to the filename.
    model.save(model_name.stem)
else:
    print(f"[+] Model {model_name.stem} exists, skipping the learning.")

# Load the model we just trained.
model = PPO.load(path=model_name.stem, env=env)
print(f"Loaded model: {model_name.stem}")

# Eval env.
# Evaluate
print("Evaluating model")
mean_reward, std_reward = evaluate_policy(
    model,
    env,
    n_eval_episodes=30,
    deterministic=True,
)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward}")

repo_id = "WhatTheFuzz/ppo-LunarLander-v2"
commit_message = "Upload PPO LunarLander-v2 trained agent"
env_id = "LunarLander-v2"
model_architecture = "PPO"

# Create the evaluation env and set the render_mode="rgb_array"
eval_env = DummyVecEnv([lambda: Monitor(gym.make(env_id, render_mode="rgb_array"))])

# PLACE the package_to_hub function you've just filled here
package_to_hub(
    model=model,  # Our trained model
    model_name="ppo-LunarLander-v2",  # The name of our trained model
    model_architecture=model_architecture,  # The model architecture we used: in our case PPO
    env_id=env_id,  # Name of the environment
    eval_env=eval_env,  # Evaluation Environment
    repo_id=repo_id,  # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
    commit_message=commit_message,
)