train.py

import ray
from ray import tune
from ray.rllib.algorithms.ppo import PPOConfig
# from envs.nautilus_env import NautilusExecutionEnv  # Not used in this script
import os

def train():
    # 1. Init Ray
    ray.init(ignore_reinit_error=True)
    
    # 2. Register Environment (Using Standard Gym for Health Check)
    env_name = "CartPole-v1" 
    
    # 3. Configure Algorithm
    config = (
        PPOConfig()
        .environment(env_name)
        .framework("torch")
        .rollouts(num_rollout_workers=0) # 0 for local test, CPU count for Prod
        .training(model={"fcnet_hiddens": [64, 64]})
        .resources(num_gpus=0) # Set to 1 if using GPU Space
    )
    
    # 4. Run Training
    print("Starting Training...")
    algo = config.build()
    
    for i in range(10):  # 10 Iterations for test
        result = algo.train()
        print(f"Iter: {i}, Reward: {result['episode_reward_mean']}")
        
        # Save Checkpoint
        if i % 5 == 0:
            checkpoint_dir = algo.save(f"./checkpoints/iter_{i}")
            print(f"Checkpoint saved at {checkpoint_dir}")

    # 5. Export to ONNX (Crucial for Nautilus)
    print("Exporting to ONNX...")
    # onnx_path = algorithm.export_model_model(export_dir="./models") 
    # (Simplified, implementation detail varies by RLLib version)
    
    ray.shutdown()

if __name__ == "__main__":
    train()