src/train.py

from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import VecNormalize
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import EvalCallback


# Create 20 parallel environments for training and normalize the rewards
env = make_vec_env('LunarLander-v2', n_envs=20)
env = VecNormalize(env, norm_reward=True)

# Create the evaluation environment and apply VecNormalize
eval_env = make_vec_env('LunarLander-v2', n_envs=1)
eval_env = VecNormalize(eval_env, norm_reward=True)

# Callback for evaluation and saving the best model
eval_callback = EvalCallback(eval_env, best_model_save_path='./logs/best_model',
                             log_path='./logs/', eval_freq=10_000,
                             deterministic=True, render=False)

# Initialize the PPO model with adjusted hyperparameters
model = PPO("MlpPolicy", env, verbose=1, device="cuda",
            batch_size=128,        # Larger batch size
            n_steps=4096,
            gae_lambda=0.98,
            gamma=0.999,
            n_epochs=4,
            ent_coef=0.01)  # Path to store TensorBoard logs


# Train the model for 4 million timesteps
model.learn(total_timesteps=5_000_000, callback=eval_callback)

# Save the trained model
model_name = "ppo-LunarLander-v2"
model.save(model_name)

env.save("vecnormalize.pkl")

# Load the best model
best_model = PPO.load("./logs/best_model/best_model.zip")

# Normalize the evaluation environment (using the same normalization as training)
eval_env.training = False
eval_env.norm_reward = False  # Turn off normalization for evaluation

# Evaluate the best model
mean_reward, std_reward = evaluate_policy(best_model, eval_env, n_eval_episodes=100, deterministic=True)
print(f"Best model mean_reward={mean_reward:.2f} +/- {std_reward}")