A Reinforcement Learning Method Supported by a Bayesian Network

A reinforcement learning (RL) is known as one of the machine learning methods, and has been applied to multi-agent problems. In this paper, we propose a new RL method using a Bayesian network (BN), which is a stochastic model and plays a role of the supervised learning procedure. An agent learns how to move under certain circumstances by an original RL method, and then the strategy is improved by using BN. We verify the effectiveness of our method by carrying out simulations for a certain multi-agent problem, and show that an agent learns its appropriate strategy for complicated tasks more effectively by using our method.