Train Robot Grasping Policies with PyBullet Physics and TensorFlow Reinforcement Learning

                      
                     
"""
Production implementation for training robot grasping policies using PyBullet physics and TensorFlow Reinforcement Learning.
Provides secure, scalable operations for simulating and training robotic grasping actions.
"""

from typing import Dict, Any, List
import os
import logging
import numpy as np
import tensorflow as tf
from pybullet_envs import bullet
import random
import time

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

# Configuration class for environment variables
class Config:
    env_id: str = os.getenv('ENV_ID', 'FetchPickAndPlace-v1')
    num_episodes: int = int(os.getenv('NUM_EPISODES', 100))
    learning_rate: float = float(os.getenv('LEARNING_RATE', 0.001))

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for training.
    
    Args:
        data: Input data containing environment and training parameters.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'episodes' not in data or data['episodes'] <= 0:
        raise ValueError('Invalid number of episodes')
    return True

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

def transform_records(states: List[List[float]]) -> np.ndarray:
    """Transform state records into numpy array for model input.
    
    Args:
        states: List of states recorded during training.
    Returns:
        Numpy array of transformed states.
    """
    return np.array(states)

def process_batch(batch: List[Dict[str, Any]]) -> Dict[str, np.ndarray]:
    """Process a batch of training data.
    
    Args:
        batch: List of training data records.
    Returns:
        Processed batch data.
    """
    states = [record['state'] for record in batch]
    actions = [record['action'] for record in batch]
    return {'states': transform_records(states), 'actions': np.array(actions)}

def aggregate_metrics(metrics: List[Dict[str, float]]) -> Dict[str, float]:
    """Aggregate metrics over episodes.
    
    Args:
        metrics: List of metrics recorded during training.
    Returns:
        Aggregated metrics.
    """
    aggregated = {key: np.mean([m[key] for m in metrics]) for key in metrics[0]}
    return aggregated

def fetch_data(env) -> Dict[str, float]:
    """Fetch data from the PyBullet environment.
    
    Args:
        env: The simulation environment.
    Returns:
        Current state and reward from the environment.
    """
    state = env.reset()
    action = env.action_space.sample()
    next_state, reward, done, _ = env.step(action)
    return {'state': state, 'reward': reward, 'done': done}

def save_to_db(data: Dict[str, Any]) -> None:
    """Save training data to a database.
    
    Args:
        data: Data to save.
    """
    # Simulated database save
    logger.info(f"Saving data: {data}")

def call_api(endpoint: str, payload: Dict[str, Any]) -> None:
    """Send data to an external API.
    
    Args:
        endpoint: API endpoint to call.
        payload: Data to send.
    """
    logger.info(f"Calling API at {endpoint} with payload: {payload}")

class RobotGraspingTrainer:
    """Main class for training robot grasping policies.
    
    Attributes:
        env: PyBullet environment instance.
        model: TensorFlow model for reinforcement learning.
    """
    def __init__(self, config: Config) -> None:
        self.env = bullet.loadURDF(config.env_id)
        self.model = self.build_model()

    def build_model(self) -> tf.keras.Model:
        """Build and compile the TensorFlow model.
        
        Returns:
            Compiled TensorFlow model.
        """
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(64, activation='relu', input_shape=(self.env.observation_space.shape[0],)),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(self.env.action_space.n, activation='softmax')
        ])
        model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=Config.learning_rate),
                      loss='categorical_crossentropy', metrics=['accuracy'])
        return model

    def train(self) -> None:
        """Train the model over multiple episodes.
        """
        for episode in range(Config.num_episodes):
            logger.info(f"Starting episode {episode + 1}/{Config.num_episodes}")
            metrics = []
            done = False
            while not done:
                data = fetch_data(self.env)
                state = data['state']
                actions = self.model.predict(np.expand_dims(state, axis=0))
                action = np.argmax(actions)
                next_data = self.env.step(action)
                metrics.append({'state': state, 'action': action, 'reward': next_data[1]})
                done = next_data[2]
            processed = process_batch(metrics)
            save_to_db(processed)
            logger.info(f"Episode {episode + 1} completed.")
        logger.info("Training completed!")

if __name__ == '__main__':
    config = Config()
    trainer = RobotGraspingTrainer(config)
    trainer.train()