DistributionalDQNnet

class torchrl.modules.DistributionalDQNnet(*args, **kwargs)

Distributional Deep Q-Network softmax layer.

This layer should be used in between a regular model that predicts the action values and a distribution which acts on logits values.

Parameters:

• in_keys (list of str or tuples of str) – input keys to the log-softmax operation. Defaults to ["action_value"].

• out_keys (list of str or tuples of str) – output keys to the log-softmax operation. Defaults to ["action_value"].

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> net = DistributionalDQNnet()
>>> td = TensorDict({"action_value": torch.randn(10, 5)}, batch_size=[10])
>>> net(td)
TensorDict(
    fields={
        action_value: Tensor(shape=torch.Size([10, 5]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([10]),
    device=None,
    is_shared=False)

forward(tensordict=None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.