Improving the robustness of reinforcement learning for a multi-robot system environment

Abstract

We have been developing a new reinforcement learning called BRL, which is especially effective to multi-robot systems (MRS). BRL has a unique feature that it not only learns in the learning space but also changes the segmentation of the learning space simultaneously. BRL has been proved to be clearly effective than the other standard RL algorithms to MRS problems where the learning environment is naturally dynamic. However, we have also noticed that MRS needs the more robustness for the learning mechanism as the complexity level increases. In this paper. BRL is extended to improve the robustness against the dynamics in a learning environment by showing a way of overcoming the unwanted feature of over-fitting. Computer simulations are conducted to illustrate the robust performance of the proposed technique.