| import abc |
| from typing import Dict |
|
|
| from mlagents.trainers.buffer import AgentBuffer |
|
|
|
|
| class Optimizer(abc.ABC): |
| """ |
| Creates loss functions and auxillary networks (e.g. Q or Value) needed for training. |
| Provides methods to update the Policy. |
| """ |
|
|
| def __init__(self): |
| self.reward_signals = {} |
|
|
| @abc.abstractmethod |
| def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]: |
| """ |
| Update the Policy based on the batch that was passed in. |
| :param batch: AgentBuffer that contains the minibatch of data used for this update. |
| :param num_sequences: Number of recurrent sequences found in the minibatch. |
| :return: A Dict containing statistics (name, value) from the update (e.g. loss) |
| """ |
| pass |
|
|
