  In Reinforcement Learning (RL), Convolutional Neural Networks(CNNs) have been
successfully applied as function approximators in Deep Q-Learning algorithms,
which seek to learn action-value functions and policies in various
environments. However, to date, there has been little work on the learning of
symmetry-transformation equivariant representations of the input environment
state. In this paper, we propose the use of Equivariant CNNs to train RL agents
and study their inductive bias for transformation equivariant Q-value
approximation. We demonstrate that equivariant architectures can dramatically
enhance the performance and sample efficiency of RL agents in a highly
symmetric environment while requiring fewer parameters. Additionally, we show
that they are robust to changes in the environment caused by affine
transformations.
