Wrap Physical Factory Robots as RL Training Environments with Gymnasium and rclpy

                      
                     
"""
Production implementation for wrapping physical factory robots as reinforcement learning training environments.
This module integrates Gymnasium and rclpy for robot control and training.
"""
from typing import Dict, Any, List, Tuple, Optional
import os
import logging
import gym
import rclpy
from rclpy.node import Node
from rclpy.qos import QualityOfServiceProfile
from time import sleep
from dataclasses import dataclass

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

class Config:
    """
    Configuration class for environment variables.
    """
    robot_topic: str = os.getenv('ROBOT_TOPIC', '/robot/control')
    gym_env: str = os.getenv('GYM_ENV', 'Robot-v0')

@dataclass
class RobotState:
    """
    Data class to represent the state of the robot.
    """
    position: Tuple[float, float]
    velocity: float
    battery_level: float

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

async def fetch_robot_state() -> RobotState:
    """Fetch the current state of the robot.
    
    Returns:
        RobotState: The current state of the robot
    """
    # Simulating fetching robot state, replace with actual logic
    return RobotState(position=(0.0, 0.0), velocity=1.0, battery_level=100.0)

async def normalize_data(state: RobotState) -> List[float]:
    """Normalize robot state data for model input.
    
    Args:
        state: The current state of the robot
    Returns:
        List[float]: Normalized state data
    """
    return [state.position[0] / 10.0, state.position[1] / 10.0, state.velocity / 10.0]

async def process_batch(states: List[RobotState]) -> None:
    """Process a batch of robot states for training.
    
    Args:
        states: List of robot states
    """
    # Process batch logic here
    logger.info(f'Processing batch of {len(states)} states...')

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    """
    # Simulating database save, replace with actual logic
    logger.info('Data saved to database')

class RobotEnv(Node):
    """Main class for the robot environment.
    Handles the integration of Gymnasium and rclpy.
    """
    def __init__(self):
        super().__init__('robot_env')
        self.config = Config()
        self.create_subscription(RobotState, self.config.robot_topic, self.listener_callback, QualityOfServiceProfile())
        self.env = gym.make(self.config.gym_env)
        self.state = RobotState(position=(0.0, 0.0), velocity=0.0, battery_level=100.0)

    def listener_callback(self, msg: RobotState) -> None:
        """Callback function for robot state updates.
        
        Args:
            msg: Incoming robot state message
        """
        logger.info('Received robot state update')
        self.state = msg
        # Here, we could implement further processing logic

    async def run_training(self) -> None:
        """Main training loop.
        Fetches robot state and runs training steps.
        """
        while True:
            state = await fetch_robot_state()  # Fetch the latest state
            normalized = await normalize_data(state)  # Normalize the state
            # Here we would include RL training logic
            await process_batch([state])  # Process the state
            await save_to_db({'state': normalized})  # Save to DB
            sleep(1)  # Simulate wait time for next iteration

if __name__ == '__main__':
    rclpy.init()
    robot_env = RobotEnv()
    try:
        robot_env.run_training()  # Start the training loop
    except Exception as e:
        logger.error(f'Error occurred: {e}')
    finally:
        rclpy.shutdown()  # Ensure rclpy is properly shut down