garage.torch.optimizers.optimizer_wrapper module

A PyTorch optimizer wrapper that compute loss and optimize module.

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

Bases: object

A wrapper class to handle torch.optim.optimizer.

Parameters:

• 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(*inputs)

Yields a batch of inputs.

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

Parameters:

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

Yields:

list[torch.Tensor] –

A list batch of inputs. Each batch has shape

\((P, *)\).

step(**closure)

Performs a single optimization step.

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

zero_grad()

Clears the gradients of all optimized torch.Tensor s.