genesis_learning.py

"""
Genesis Learning Framework

Learning framework with control policy, tasks, and optimizer for gradient-based
control policy learning with Genesis's differentiable physics.
"""

import logging
from typing import Callable, Optional, Dict, Any
import numpy as np

# Configure module-level logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

if not logger.handlers:
    handler = logging.StreamHandler()
    handler.setLevel(logging.INFO)
    formatter = logging.Formatter('%(name)s - %(levelname)s - %(message)s')
    handler.setFormatter(formatter)
    logger.addHandler(handler)


class ControlPolicy:
    """
    Neural network control policy for generating actions from state observations.

    Uses a simple two-layer feedforward network with ReLU activation in hidden layer
    and tanh output for action constraint to [-1, 1].

    Attributes:
        input_dim (int): Dimension of input state
        output_dim (int): Dimension of output actions
        hidden_dim (int): Dimension of hidden layer
        learning_rate (float): Learning rate for optimization
        w1 (np.ndarray): First layer weights, shape (input_dim, hidden_dim)
        b1 (np.ndarray): First layer bias, shape (1, hidden_dim)
        w2 (np.ndarray): Second layer weights, shape (hidden_dim, output_dim)
        b2 (np.ndarray): Second layer bias, shape (1, output_dim)
    """

    def __init__(self, input_dim: int, output_dim: int, hidden_dim: int = 64, learning_rate: float = 0.001):
        """
        Initialize control policy with random weights.

        Args:
            input_dim (int): Dimension of input state
            output_dim (int): Dimension of output actions
            hidden_dim (int): Dimension of hidden layer (default: 64)
            learning_rate (float): Learning rate for optimization (default: 0.001)
        """
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dim = hidden_dim
        self.learning_rate = learning_rate

        # Initialize weights with small random values (Xavier-like initialization)
        self.w1 = np.random.randn(input_dim, hidden_dim) * 0.01
        self.b1 = np.zeros((1, hidden_dim))
        self.w2 = np.random.randn(hidden_dim, output_dim) * 0.01
        self.b2 = np.zeros((1, output_dim))

        logger.info(
            f"ControlPolicy initialized: input_dim={input_dim}, output_dim={output_dim}, "
            f"hidden_dim={hidden_dim}, learning_rate={learning_rate}"
        )

    def forward(self, state: np.ndarray) -> np.ndarray:
        """
        Forward pass through policy network.

        Computes: action = tanh(ReLU(state @ w1 + b1) @ w2 + b2)

        The tanh activation constrains actions to [-1, 1].

        Args:
            state (np.ndarray): Input state, shape (batch_size, input_dim)

        Returns:
            np.ndarray: Output actions, shape (batch_size, output_dim), values in [-1, 1]
        """
        # Hidden layer with ReLU activation
        hidden = np.maximum(0, state @ self.w1 + self.b1)  # ReLU

        # Output layer with tanh activation (constrains to [-1, 1])
        action = np.tanh(hidden @ self.w2 + self.b2)

        return action

    def get_parameters(self) -> Dict[str, np.ndarray]:
        """
        Get all policy parameters.

        Returns:
            Dict[str, np.ndarray]: Dictionary of parameter name to array
        """
        return {
            'w1': self.w1.copy(),
            'b1': self.b1.copy(),
            'w2': self.w2.copy(),
            'b2': self.b2.copy(),
        }

    def set_parameters(self, params: Dict[str, np.ndarray]) -> None:
        """
        Set policy parameters from dictionary.

        Args:
            params (Dict[str, np.ndarray]): Dictionary of parameter name to array
        """
        if 'w1' in params:
            self.w1 = params['w1']
        if 'b1' in params:
            self.b1 = params['b1']
        if 'w2' in params:
            self.w2 = params['w2']
        if 'b2' in params:
            self.b2 = params['b2']

        logger.info("ControlPolicy parameters updated")


class LearningTask:
    """
    Definition of a learning task with loss function and evaluation metrics.

    Attributes:
        name (str): Task name
        loss_function (Callable): Function that computes loss from (world_state, target_state)
        max_steps (int): Maximum simulation steps per episode
        target_state (Optional[np.ndarray]): Optional target state for the task
        episode_losses (list): List of cumulative losses for each episode
    """

    def __init__(
        self,
        name: str,
        loss_function: Callable[[np.ndarray, np.ndarray], float],
        max_steps: int = 1000,
        target_state: Optional[np.ndarray] = None
    ):
        """
        Initialize learning task.

        Args:
            name (str): Task name
            loss_function (Callable): Function that computes loss from (world_state, target_state)
            max_steps (int): Maximum simulation steps per episode (default: 1000)
            target_state (Optional[np.ndarray]): Optional target state (default: None)
        """
        self.name = name
        self.loss_function = loss_function
        self.max_steps = max_steps
        self.target_state = target_state
        self.episode_losses = []

        logger.info(f"LearningTask created: name={name}, max_steps={max_steps}")

    def evaluate(
        self,
        policy: ControlPolicy,
        simulator: Any,
        initial_state: np.ndarray
    ) -> Optional[float]:
        """
        Evaluate policy performance on this task.

        TODO: Implement actual episode evaluation.
        Expected flow:
        - Run simulation for max_steps with policy control
        - At each step, compute loss using loss_function
        - Accumulate cumulative loss
        - Return total cumulative loss

        Args:
            policy (ControlPolicy): Policy to evaluate
            simulator (Any): Simulator instance (Genesis or mock)
            initial_state (np.ndarray): Initial world state

        Returns:
            Optional[float]: Cumulative loss for the episode (None if not implemented)
        """
        # Placeholder for actual implementation
        pass


class GradientOptimizer:
    """
    Optimizer for policy gradient-based learning.

    Performs optimization steps on policy parameters using computed gradients.

    Attributes:
        policy (ControlPolicy): Policy to optimize
        learning_rate (float): Learning rate for optimization steps
    """

    def __init__(self, policy: ControlPolicy, learning_rate: float = 0.001):
        """
        Initialize gradient optimizer.

        Args:
            policy (ControlPolicy): Policy to optimize
            learning_rate (float): Learning rate for optimization (default: 0.001)
        """
        self.policy = policy
        self.learning_rate = learning_rate

        logger.info(f"GradientOptimizer initialized with learning_rate={learning_rate}")

    def step(self, loss: float) -> None:
        """
        Perform one optimization step.

        TODO: Implement actual gradient computation and parameter update.
        Expected flow:
        - Compute gradients of loss with respect to policy parameters
        - Use Genesis differentiable simulator to backpropagate through physics
        - Update policy parameters using gradient descent

        Args:
            loss (float): Loss value from task evaluation

        Raises:
            NotImplementedError: If genesis integration for autodiff is not available
        """
        logger.info(f"GradientOptimizer.step() called with loss={loss}")
        # Placeholder for actual implementation
        pass