Coordinated learning based on time-sharing tracking framework and Gaussian regression for continuous multi-agent systems

Applying multi-agent reinforcement learning (MARL) in continuous distributed control system is an attractive issue, because it entitles agents adaptively to construct a cooperative behavior, even if the dynamics of such distributed system is unknown a priori. However the implementation of MARL always suffers from dimension explosion, nonstationary learning, and generalization in continuous systems. This paper presents a continuous coordinated learning algorithm with time-sharing tracking framework (CCL-TT) to deal with these problems, in which the value function is dimension reduced to lighten dimension explosion, the time-sharing tracking framework (TTF) is developed to solve nonstationary learning, and Gaussian regression modeling is applied to realize generalization. With TTF, a macroscopic concurrent learning is set up to meet the requirements of temporal stationary condition in value learning and generalization. Finally the simulation illustrates how CCL-TT realizes cooperative learning without knowledge about the dynamics of the system, even with disturbance.