Performance improvement of evolution strategies using reinforcement learning

Abstract

In this paper, we propose a new type of evolution strategies combined with reinforcement learning. We use the change of fitness occurred by mutation to make the reinforcement signals which estimate and control the step length of mutation. With this proposed method, the convergence rate is improved. Also, we use Cauchy distributed mutation to increase global convergence faculty. Cauchy distributed mutation is more likely to escape from a local minimum or move away from a plateau than Gaussian distributed mutation. After an outline of the history of evolution strategies, we will explain the evolution strategies combined with the reinforcement learning, that is reinforcement evolution strategies. The performance of proposed method will be estimated by comparison with conventional evolution strategies on several test problems.