Neighboring crossover to improve GA-based Q-learning method for multi-legged robot control

Abstract

In this paper, we propose a crossover method to improve a GA-based Q-learning method for controlling multi-legged robots. As a GA-based Q-learning method, we employ a method called "Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)". We propose a crossover for QDSEGA, and a method to reward a robot in Q-learning in order to follow a moving target. Simulation results clearly show the effectiveness of the proposed methods.