Train Robotic Assembly Skills in Simulation with robosuite and Stable-Baselines3

                      
                     
"""
Production implementation for training robotic assembly skills in simulation using robosuite and Stable-Baselines3.
Provides secure, scalable operations.
"""
import os
import logging
from typing import Dict, Any, List
import gym
import robosuite as suite
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv

# Logger setup for tracking events
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    Loads configuration from environment.
    """
    # Define environment variables for configuration
    ROBOSUITE_ENV: str = os.getenv('ROBOSUITE_ENV', 'Lift')
    TOTAL_EPISODES: int = int(os.getenv('TOTAL_EPISODES', '100'))

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

def create_environment(env_name: str) -> gym.Env:
    """Create a robosuite environment.
    
    Args:
        env_name: Name of the robosuite environment to create
    Returns:
        A gym-compatible environment instance
    """
    try:
        env = suite.make(env_name)
        logger.info(f'Environment {env_name} created successfully.')
        return env
    except Exception as e:
        logger.error(f'Error creating environment: {e}')
        raise

def normalize_data(observation: Dict[str, Any]) -> List[float]:
    """Normalize the observation data.
    
    Args:
        observation: Raw observation data
    Returns:
        Normalized observation as a list
    """
    normalized = [observation['robot0_eef_pos'][0], observation['robot0_eef_pos'][1], 
                  observation['robot0_eef_pos'][2]]
    logger.debug('Normalized observation data.')
    return normalized

def train_agent(env: gym.Env) -> PPO:
    """Train a PPO agent in the given environment.
    
    Args:
        env: The environment to train in
    Returns:
        Trained PPO agent
    """
    model = PPO('MlpPolicy', env, verbose=1)
    model.learn(total_timesteps=10000)  # Train the model
    logger.info('Agent trained successfully.')
    return model

def save_model(model: PPO, filename: str) -> None:
    """Save the trained model to a file.
    
    Args:
        model: The model to save
        filename: Filename for saving the model
    """
    model.save(filename)
    logger.info(f'Model saved to {filename}.')

def run_training_pipeline(config: Config) -> None:
    """Run the complete training pipeline.
    
    Args:
        config: Configuration object with env details
    """
    try:
        env = create_environment(config.ROBOSUITE_ENV)
        vec_env = DummyVecEnv([lambda: env])  # Vectorized environment
        model = train_agent(vec_env)
        save_model(model, 'robotic_assembly_model')  # Save the model
    except Exception as e:
        logger.error(f'Error in training pipeline: {e}')
        raise

if __name__ == '__main__':
    # Example usage of pipeline
    config = Config()
    run_training_pipeline(config)  # Start the training process

# The end of the script marks where the code execution begins.