Performance improvement of evolution strategies using reinforcement learning

We propose a new type of evolution strategies combined with reinforcement learning. We use the change of fitness occurred by mutation to form 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 the 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 explain the evolution strategies combined with the reinforcement learning, that is reinforcement evolution strategies. Performance of the proposed method is estimated by comparison with conventional evolution strategies on several test problems.