Reinforcement Learning for Topology Optimization of a Synchronous Reluctance Motor

Abstract

In this article, a method for topology optimization (TO) of a synchronous reluctance motor (SynRM) is proposed using deep reinforcement learning (RL). Due to the need for simulating a large number of finite-element models in a traditional TO task, incorporating a study involving a different problem formulation (such as a varying design domain) can be an overwhelming task. A neural network (NN)-based agent trained using an RL formulation is able to extend the knowledge from one TO design problem to other similar TO tasks. The applicability of such learning is performed using a sequence-based TO environment. It is observed that such an approach not only reduces the computation required for TO, but also introduces the capability to generalize RL to unseen TO scenarios. The proposed optimization method reduces computation time by 70%–90% when compared to a genetic algorithm-based implementation.