Shortcuts

torchrl.trainers.algorithms.configs.utils.AdagradConfig

class torchrl.trainers.algorithms.configs.utils.AdagradConfig(lr: float = 0.01, lr_decay: float = 0.0, weight_decay: float = 0.0, initial_accumulator_value: float = 0.0, eps: float = 1e-10, maximize: bool = False, foreach: bool | None = None, differentiable: bool = False, _target_: str = 'torch.optim.Adagrad', _partial_: bool = True)[source]

Configuration for Adagrad optimizer.

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources