  Visual exploration is a task that seeks to visit all the navigable areas of
an environment as quickly as possible. The existing methods employ deep
reinforcement learning (RL) as the standard tool for the task. However, they
tend to be vulnerable to statistical shifts between the training and test data,
resulting in poor generalization over novel environments that are
out-of-distribution (OOD) from the training data. In this paper, we attempt to
improve the generalization ability by utilizing the inductive biases available
for the task. Employing the active neural SLAM (ANS) that learns exploration
policies with the advantage actor-critic (A2C) method as the base framework, we
first point out that the mappings represented by the actor and the critic
should satisfy specific symmetries. We then propose a network design for the
actor and the critic to inherently attain these symmetries. Specifically, we
use $G$-convolution instead of the standard convolution and insert the
semi-global polar pooling (SGPP) layer, which we newly design in this study, in
the last section of the critic network. Experimental results show that our
method increases area coverage by $8.1 m^2$ when trained on the Gibson dataset
and tested on the MP3D dataset, establishing the new state-of-the-art.
