Reinforcement learning in zero-sum Markov games for robot soccer systems

摘要

The objective of this paper is to develop a strategy system in a robot soccer system with cooperative ability which is improved by self-learning. A reinforcement learning method according to the zero-sum game theory is developed in this paper. It enforces the learning systems to choose appropriate strategy on the opponent's actions. In order to achieve the purpose of cooperation, two sub systems have been used, one is a role assignment system and the other one is a reinforcement learning system.