Instance-based reinforcement learning technique with a meta-learning mechanism for robust multi-robot systems

Abstract

In recent years, the subject of learning autonomous robots has been widely discussed. Reinforcement learning (RL) is a popular method in this domain. However, its performance is quite sensitive to the discretization of state and action spaces. To overcome this problem, we have developed a new technique called Bayesian-discrimination-function-based RL (BRL). BRL has proven to be more effective than other standard RL algorithms in dealing with multi-robot system (MRS) problems. However, similar to most learning systems, BRL occasionally suffers from overfitting. This paper introduces an extension of BRL for improving the robustness of MRSs. Meta-learning based on the information entropy of firing rules is adopted for adaptively modifying its learning parameters. Physical experiments are conducted to verify the effectiveness of our proposed method. © 2011 Springer-Verlag Berlin Heidelberg.