|Co-Operative Strategy for an Interactive Robot Soccer System by Reinforcement Learning Method
Hyoung-Rock Kim, Jung-Hoon Hwang, and Dong-Soo Kwon*
International Journal of Control, Automation, and Systems, vol. 1, no. 2, pp.236-242, 2003
Abstract : This paper presents a cooperation strategy between a human operator and autonomous
robots for an interactive robot soccer game. The interactive robot soccer game has been
developed to allow humans to join into the game dynamically and reinforce entertainment characteristics.
In order to make these games more interesting, a cooperation strategy between humans
and autonomous robots on a team is very important. Strategies can be pre-programmed or
learned by robots themselves with learning or evolving algorithms. Since the robot soccer system
is hard to model and its environment changes dynamically, it is very difficult to pre-program
cooperation strategies between robot agents. Q-learning - one of the most representative reinforcement
learning methods - is shown to be effective for solving problems dynamically without
explicit knowledge of the system. Therefore, in our research, a Q-learning based learning
method has been utilized. Prior to utilizing Q-learning, state variables describing the game situation
and actions’ sets of robots have been defined. After the learning process, the human operator
could play the game more easily. To evaluate the usefulness of the proposed strategy, some
simulations and games have been carried out.
Keyword : Robot soccer, reinforcement learning, human-robot cooperation, entertainment robots.