Simulate and Train Warehouse AMR Fleets with Genesis and Stable Baselines3

"""
Production implementation for simulating and training warehouse AMR fleets.
Provides a robust framework using Genesis and Stable Baselines3.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import random
from stable_baselines3 import PPO
from stable_baselines3.common.envs import DummyVecEnv
from genesis import WarehouseEnv

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    warehouse_size: Tuple[int, int] = (10, 10)  # Warehouse dimensions
    num_agents: int = int(os.getenv('NUM_AGENTS', 5))  # Number of AMRs

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for simulation.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'warehouse_size' not in data or not isinstance(data['warehouse_size'], (tuple, list)):
        raise ValueError('Invalid warehouse size')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

def create_environment(size: Tuple[int, int]) -> DummyVecEnv:
    """Create a warehouse environment for AMRs.
    
    Args:
        size: Dimensions of the warehouse
    Returns:
        The wrapped environment
    """
    env = WarehouseEnv(size=size)
    return DummyVecEnv([lambda: env])  # Wrap in DummyVecEnv for compatibility

def train_agents(env: DummyVecEnv, total_timesteps: int = 10000) -> PPO:
    """Train AMR agents using PPO algorithm.
    
    Args:
        env: The environment for training
        total_timesteps: Number of timesteps for training
    Returns:
        The trained PPO model
    """
    model = PPO('MlpPolicy', env, verbose=1)
    model.learn(total_timesteps=total_timesteps)
    return model

def simulate_agents(model: PPO, env: DummyVecEnv, num_episodes: int = 5) -> List[float]:
    """Simulate AMR agents in the environment.
    
    Args:
        model: The trained PPO model
        env: The environment for simulation
        num_episodes: Number of episodes to simulate
    Returns:
        List of rewards obtained in each episode
    """
    rewards = []
    for episode in range(num_episodes):
        obs = env.reset()
        done = False
        total_reward = 0
        while not done:
            action, _ = model.predict(obs)
            obs, reward, done, _ = env.step(action)
            total_reward += reward
        rewards.append(total_reward)
    return rewards

def log_results(rewards: List[float]) -> None:
    """Log simulation results to the console.
    
    Args:
        rewards: List of rewards obtained during simulation
    """
    for i, reward in enumerate(rewards):
        logger.info(f'Episode {i + 1}: Total Reward = {reward}')  # Log each episode's reward

def save_model(model: PPO, model_path: str) -> None:
    """Save the trained model to a file.
    
    Args:
        model: The trained PPO model
        model_path: Path to save the model
    """
    model.save(model_path)  # Use Stable Baselines3's save method
    logger.info(f'Model saved to {model_path}')

class WarehouseAMR:
    """Main class to simulate and train AMR fleets.
    """
    def __init__(self, config: Config):
        self.config = config
        self.env = create_environment(config.warehouse_size)
        self.model = None

    def run_simulation(self):
        """Run the complete simulation workflow.
        """
        try:
            logger.info('Starting training...')
            self.model = train_agents(self.env)
            logger.info('Training completed.')

            logger.info('Simulating agents...')
            rewards = simulate_agents(self.model, self.env)
            log_results(rewards)  # Log results

            save_model(self.model, 'trained_amr_model')  # Save the model
        except Exception as e:
            logger.error(f'An error occurred: {e}')  # Handle errors gracefully

if __name__ == '__main__':
    # Example usage
    config = Config()
    amr_simulation = WarehouseAMR(config)
    amr_simulation.run_simulation()  # Execute the simulation