Q-learning in multi-agent cooperation

Abstract

Q-learning, a most widely used reinforcement learning method, normally needs well-defined quantized state and action spaces to obtain an optimal policy for accomplishing a given task. This means it difficult to be applied to real robot tasks because of poor performance of learned behavior due to the failure of quantization of continuous state and action spaces. In this paper, we proposed a fuzzy-based CMAC method to calculate contribution values to estimate a continuous action value in order to make motion smooth and effective. And we implement it to a multi-agent system for real robot applications.