Train Factory Floor Navigation Agents with MuJoCo and Stable-Baselines3

                      
                     
"""
Production implementation for training factory floor navigation agents using MuJoCo and Stable-Baselines3.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
import numpy as np

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to handle environment variables.
    """
    def __init__(self) -> None:
        self.env_id: str = os.getenv('ENV_ID', 'MuJoCoEnv')
        self.model_path: str = os.getenv('MODEL_PATH', './models/ppo_model')
        self.training_timesteps: int = int(os.getenv('TRAINING_TIMESTEPS', 100000))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for training.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'env_id' not in data:
        raise ValueError('Missing env_id in input data')
    return True

def fetch_data(env_id: str) -> gym.Env:
    """Fetch the gym environment based on provided ID.
    
    Args:
        env_id: Identifier for the gym environment
    Returns:
        Environment instance
    """
    try:
        env = gym.make(env_id)
        return env
    except Exception as e:
        logger.error(f'Failed to create environment: {e}')
        raise

def save_model(model: PPO, path: str) -> None:
    """Save the trained model to specified path.
    
    Args:
        model: The trained PPO model
        path: Where to save the model
    """
    try:
        model.save(path)
        logger.info(f'Model saved to {path}')
    except Exception as e:
        logger.error(f'Error saving model: {e}')
        raise

def load_model(path: str) -> PPO:
    """Load a pre-trained model from the specified path.
    
    Args:
        path: Path to the saved model
    Returns:
        Loaded model instance
    """
    try:
        model = PPO.load(path)
        logger.info(f'Model loaded from {path}')
        return model
    except Exception as e:
        logger.error(f'Error loading model: {e}')
        raise

def train_agent(env: gym.Env, model: PPO, timesteps: int) -> PPO:
    """Train the agent using the specified environment and model.
    
    Args:
        env: The environment to train in
        model: The PPO model
        timesteps: Number of training timesteps
    Returns:
        Trained model
    """
    try:
        model.learn(total_timesteps=timesteps)
        logger.info('Training complete')
        return model
    except Exception as e:
        logger.error(f'Training failed: {e}')
        raise

def evaluate_agent(model: PPO, env: gym.Env) -> Tuple[float, float]:
    """Evaluate the trained agent in the environment.
    
    Args:
        model: The trained PPO model
        env: The environment to evaluate in
    Returns:
        Average reward and standard deviation
    """
    try:
        mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=10)
        logger.info(f'Evaluation complete: mean={mean_reward}, std={std_reward}')
        return mean_reward, std_reward
    except Exception as e:
        logger.error(f'Evaluation failed: {e}')
        raise

class NavigationAgent:
    """Main orchestrator for training navigation agents.
    """
    def __init__(self, config: Config) -> None:
        self.config = config
        self.env = fetch_data(self.config.env_id)
        self.model = PPO('MlpPolicy', self.env, verbose=1)

    def run_training(self) -> None:
        """Execute the training pipeline.
        """
        try:
            self.model = train_agent(self.env, self.model, self.config.training_timesteps)
            save_model(self.model, self.config.model_path)
        except Exception as e:
            logger.error(f'Training process failed: {e}')

    def run_evaluation(self) -> None:
        """Execute the evaluation of the trained agent.
        """
        try:
            mean_reward, std_reward = evaluate_agent(self.model, self.env)
            logger.info(f'Mean reward: {mean_reward}, Std reward: {std_reward}')
        except Exception as e:
            logger.error(f'Evaluation process failed: {e}')

if __name__ == '__main__':
    config = Config()
    agent = NavigationAgent(config)
    agent.run_training()  # Start training
    agent.run_evaluation()  # Evaluate the agent