STRUCTURAL OPTIMIZATION BY REINFORCEMENT LEARNING AGENTS FOR COMPETITIVE GAMES

Abstract

In this paper, a new structural optimization method by constructing an agent to play a game by reinforce-ment learning is proposed. First, the outline of the competitive structure optimization game is explained, and then, the composition of the agent and learning plan is described. In addition, by confirming the play result of the game by the constructed reinforcement agent, the possibility of structural optimization by the proposed method is verified. As a result, it was confirmed that there was a possibility to construct a structure optimization agent by reinforcement learning, though there was a problem in generalization performance.