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| """Abstract base class for RL algorithms.""" | |
| from abc import ABC, abstractmethod | |
| from typing import Dict, Any | |
| import torch | |
| class RLAlgorithm(ABC): | |
| """ | |
| Abstract base class for reinforcement learning algorithms. | |
| Defines the interface that all RL algorithms must implement | |
| for training voice models. | |
| """ | |
| def __init__(self, learning_rate: float, **kwargs): | |
| """ | |
| Initialize the RL algorithm. | |
| Args: | |
| learning_rate: Learning rate for optimization | |
| **kwargs: Additional algorithm-specific parameters | |
| """ | |
| self.learning_rate = learning_rate | |
| self.hyperparameters = kwargs | |
| def compute_loss( | |
| self, | |
| states: torch.Tensor, | |
| actions: torch.Tensor, | |
| rewards: torch.Tensor, | |
| next_states: torch.Tensor, | |
| **kwargs | |
| ) -> torch.Tensor: | |
| """ | |
| Compute the loss for the current batch. | |
| Args: | |
| states: Current states | |
| actions: Actions taken | |
| rewards: Rewards received | |
| next_states: Next states | |
| **kwargs: Additional algorithm-specific inputs | |
| Returns: | |
| Loss tensor | |
| """ | |
| pass | |
| def update_policy(self, loss: torch.Tensor) -> Dict[str, Any]: | |
| """ | |
| Update the policy based on computed loss. | |
| Args: | |
| loss: Computed loss tensor | |
| Returns: | |
| Dictionary containing update metrics (e.g., gradient norms) | |
| """ | |
| pass | |
| def get_hyperparameters(self) -> Dict[str, Any]: | |
| """ | |
| Get the hyperparameters for this algorithm. | |
| Returns: | |
| Dictionary of hyperparameter names and values | |
| """ | |
| return { | |
| 'learning_rate': self.learning_rate, | |
| **self.hyperparameters | |
| } | |
| def set_hyperparameter(self, name: str, value: Any) -> None: | |
| """ | |
| Set a hyperparameter value. | |
| Args: | |
| name: Hyperparameter name | |
| value: New value | |
| """ | |
| if name == 'learning_rate': | |
| self.learning_rate = value | |
| else: | |
| self.hyperparameters[name] = value | |