alibi.models.pytorch.actor_critic

This module contains the Pytorch implementation of actor-critic networks used in the Counterfactual with Reinforcement Learning for both data modalities. The models' architectures follow the standard actor-critic design and can have broader use-cases.

Actor

Inherits from: Module

Actor network. The network follows the standard actor-critic architecture used in Deep Reinforcement Learning. The model is used in Counterfactual with Reinforcement Learning (CFRL) for both data modalities (images and tabular). The hidden dimension used for the all experiments is 256, which is a common choice in most benchmarks.

Constructor

Actor(self, hidden_dim: int, output_dim: int) -> None

Name	Type	Default	Description
hidden_dim	int		Hidden dimension.
output_dim	int		Output dimension

Methods

forward

forward(x: torch.Tensor) -> torch.Tensor

Name	Type	Default	Description
x	torch.Tensor		Input tensor.

Returns

• Type: torch.Tensor

Critic

Inherits from: Module

Critic network. The network follows the standard actor-critic architecture used in Deep Reinforcement Learning. The model is used in Counterfactual with Reinforcement Learning (CFRL) for both data modalities (images and tabular). The hidden dimension used for the all experiments is 256, which is a common choice in most benchmarks.

Constructor

Critic(self, hidden_dim: int)

Name	Type	Default	Description
hidden_dim	int		Hidden dimension.

Methods

forward

forward(x: torch.Tensor) -> torch.Tensor

Name	Type	Default	Description
x	torch.Tensor		Input tensor.

Returns

• Type: torch.Tensor