A PyTorch optimizer wrapper that compute loss and optimize module.

class OptimizerWrapper(optimizer, module, max_optimization_epochs=1, minibatch_size=None)

A wrapper class to handle torch.optim.optimizer.

  • optimizer (Union[type, tuple[type, dict]]) – Type of optimizer for policy. This can be an optimizer type such as torch.optim.Adam or a tuple of type and dictionary, where dictionary contains arguments to initialize the optimizer. e.g. (torch.optim.Adam, {‘lr’ : 1e-3}) Sample strategy to be used when sampling a new task.

  • module (torch.nn.Module) – Module to be optimized.

  • max_optimization_epochs (int) – Maximum number of epochs for update.

  • minibatch_size (int) – Batch size for optimization.

get_minibatch(self, *inputs)

Yields a batch of inputs.

Notes: P is the size of minibatch (self._minibatch_size)


*inputs (list[torch.Tensor]) – A list of inputs. Each input has shape \((N \dot [T], *)\).



A list batch of inputs. Each batch has shape

\((P, *)\).


Clears the gradients of all optimized torch.Tensor s.

step(self, **closure)

Performs a single optimization step.


**closure (callable, optional) – A closure that reevaluates the model and returns the loss.