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.

本文提出使用 Equivariant CNNs 训练强化学习智能体并研究其在对称变换方面的归纳偏差，结果表明在高度对称的环境中，使用 Equivariant CNNs 可以显著提高智能体的性能和样本效率，同时还需要更少的参数，而且它们对仿射变换引起的环境变化具有鲁棒性。