Multiagent reinforcement learning with organizational-learning oriented classifier system

Abstract

Organizational learning oriented classifier system (OCS) is a new architecture proposed by us for an evolutionary computational model. We have shown its effectiveness in large scale problems with printed circuit board (PCB) redesign using computer aided design (CAD). The paper proposes a novel reinforcement learning method for multiagents with OCS for more practical and engineering use. To validate the effectiveness of our method, we have conducted experiments on real scale PCB design problems for electric appliances. The experimental results have suggested that: (1) our method has found feasible solutions with the same quality of those by human experts; (2) the solutions are globally better than those by the conventional reinforcement learning methods with regard to both the total wiring length and the number of iterations.