In reinforcement learning (RL), exploiting environmental symmetries can
significantly enhance efficiency, robustness, and performance. However,
ensuring that the deep RL policy and value networks are respectively
equivariant and invariant to exploit these symmetries is a substantial
challenge. Related works try to design networks that are equivariant and
invariant by construction, limiting them to a very restricted library of
components, which in turn hampers the expressiveness of the networks. This
paper proposes a method to construct equivariant policies and invariant value
functions without specialized neural network components, which we term
equivariant ensembles. We further add a regularization term for adding
inductive bias during training. In a map-based path planning case study, we
show how equivariant ensembles and regularization benefit sample efficiency and
performance.

强化学习中，利用环境对称性可以提高效率、鲁棒性和性能。本文提出了一种构建等变策略和不变值函数的方法，而无需使用专门的神经网络组件，并在训练过程中添加了正则化项以增加归纳偏置。通过基于地图的路径规划案例研究，展示了等变集合和正则化对样本效率和性能的改进。