An Use of Reinforcement Learning in a Multi-Agent Environment.
kenichiroh KAWAKAMI, Kazuhiro Ohkura, Kanji Ueda · TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C · 2003
In order to give more adaptability to a multiagent system, it would be desirable for each agent to have some kind of online learning ability for how to help and guide the other agents, i.e., how to cooperate with each other for the purpose of achieving a task which is given to the whole system. When we browse the field of computational methods of online learning, reinforcement learning seems a good candidate for this mechanism. However, due to the theoretical limitation that it assumes that an environment is Markovian, traditional reinforcement learning algorithms cannot be applied directly to this behavior acquisition problem in a multiagent environment. In this paper, an online learning mechanism is designed by using two reinforcement learning units. The first one is for predicting the move of the other agents at the next time-step, and the second one is for building the appropriate action rule set for an agent itself. Several computer simulations of the cooperative carrying problem are conducted to investigate the effectiveness of the proposed approach.