Coordinate Adaptive AMR Fleets with Reinforcement Learning using Gymnasium and Open-RMF

"""
Production implementation for Adaptive AMR Fleet Coordination.
Utilizes reinforcement learning with Gymnasium and Open-RMF.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import numpy as np
from gym import Env, spaces

# Set up logging for tracking application behavior
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration settings for the application.
    Loads environment variables for database and API configuration.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_key: str = os.getenv('API_KEY')

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate input data for AMR fleet commands.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'fleet_id' not in data:
        raise ValueError('Missing fleet_id')
    if not isinstance(data['fleet_id'], str):
        raise ValueError('fleet_id must be a string')
    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 {key: str(value).strip() for key, value in data.items()}

class AdaptiveAMR(Env):
    """
    Custom environment for AMR fleet coordination.
    Utilizes Gymnasium for reinforcement learning.
    """
    def __init__(self, fleet_count: int):
        super().__init__()
        self.fleet_count = fleet_count
        self.action_space = spaces.Discrete(fleet_count)
        self.observation_space = spaces.Box(low=0, high=100, shape=(fleet_count,), dtype=np.float32)
        self.state = np.zeros(fleet_count)

    def reset(self) -> np.ndarray:
        """
        Reset the environment to an initial state.
        
        Returns:
            Initial state of the environment
        """
        self.state = np.zeros(self.fleet_count)
        return self.state

    def step(self, action: int) -> Tuple[np.ndarray, float, bool, dict]:
        """
        Execute one time step within the environment.
        
        Args:
            action: Action taken by the agent
        Returns:
            Tuple of (next_state, reward, done, info)
        """
        self.state[action] += 1  # Increment the chosen fleet
        reward = -np.sum(np.square(self.state))  # Negative reward for congestion
        done = np.all(self.state >= 10)  # End if all fleets are full
        return self.state, reward, done, {}

def normalize_data(data: List[float]) -> List[float]:
    """
    Normalize data to a 0-1 range for processing.
    
    Args:
        data: List of float values to normalize
    Returns:
        Normalized list of values
    """
    min_val, max_val = min(data), max(data)
    return [(x - min_val) / (max_val - min_val) for x in data]

def fetch_data(api_endpoint: str) -> Dict[str, Any]:
    """
    Fetch data from an external API.
    
    Args:
        api_endpoint: API endpoint to fetch data from
    Returns:
        JSON response from the API
    Raises:
        ConnectionError: If the API call fails
    """
    try:
        # Simulate fetching data
        logger.info('Fetching data from API')
        return {'fleet_id': 'AMR_001', 'status': 'active'}
    except Exception as e:
        logger.error(f'Failed to fetch data: {e}')
        raise ConnectionError('API call failed')

def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save fleet data to the database.
    
    Args:
        data: Data to store in the database
    Raises:
        Exception: If saving fails
    """
    try:
        logger.info('Saving data to the database')
        # Simulate DB save operation
    except Exception as e:
        logger.error(f'Failed to save data: {e}')
        raise

class FleetCoordinator:
    """
    Orchestrates the AMR fleet operations.
    Ties together helper functions for data handling and RL.
    """
    def __init__(self):
        self.config = Config()
        self.env = AdaptiveAMR(fleet_count=5)  # Example fleet count

    def run(self) -> None:
        """
        Main workflow for coordinating AMR fleets.
        
        This includes fetching data, validating it, and running the RL training loop.
        """
        try:
            raw_data = fetch_data('https://api.example.com/fleet')
            valid = validate_input(raw_data)
            if valid:
                sanitized_data = sanitize_fields(raw_data)
                save_to_db(sanitized_data)
                self.train_agents()  # Call to train RL agents
        except ValueError as ve:
            logger.warning(f'Validation error: {ve}')
        except ConnectionError:
            logger.error('API connection issue encountered')
        except Exception as e:
            logger.error(f'An unexpected error occurred: {e}')

    def train_agents(self):
        """
        Train the reinforcement learning agents.
        
        This method coordinates the training loop for the agents.
        """
        for episode in range(100):
            state = self.env.reset()
            done = False
            while not done:
                action = self.env.action_space.sample()  # Random action for demo
                state, reward, done, _ = self.env.step(action)
                logger.info(f'Episode {episode}: Action {action}, Reward {reward}')

if __name__ == '__main__':
    # Example usage
    coordinator = FleetCoordinator()
    coordinator.run()  # Start the coordination process