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)[source]¶ 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)[source]¶ 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
\((P, *)\).