Behavioral-fusion control based on reinforcement learning

To design appropriate actions of mobile robots, the designers usually observe the sensory signals on the robots and decide the actions from the viewpoint of some desired purposes. This approach needs deliberative consideration and abundant knowledge on robotics for a variety of situations. To improve the actions of robots, it is hard to sense the error by human eyes and takes time in trial-and-error. In this article, we propose a novel learning algorithm, fused behavior Q-learning algorithm (FBQL) to deal with such situations. The proposed algorithm has the merit of simplicity in designing individual behavior by means of a decision tree approach to state aggregation which is eventually recoding the domain knowledge. Furthermore, these learned behaviors are fused into a more complicated behavior by a set of appropriate weighting parameters through a Q-learning mechanism such that the robots can behave adaptively and optimally in a dynamic environment.