A Collaborative Learning Automata Team Model for Modeling Multi‐Agent Systems.

WANG, C. E. N.; KOAKUTSU, S. E. I. I. C. H. I.; OKAMOTO, T. A. K. A. S. H. I.; QIAN, F. E. I.

SUMMARY: The learning automaton (LA) team model has been proposed as one method for modeling multi‐agent systems. It is modeled as a noncooperative game of learning automata. In this model, each LA operates independently from each other, and there exists a Nash equilibrium, that is, the existence of an optimal mixed strategy in the mixed strategy space of the game has been proven. However, for modeling multi‐agent systems more generally, the information exchange among agents and the acquisition of cooperative behaviors such as the formation of autonomous community are required. In this paper, in order to complement the LA team model, we propose a new LA team model with some fully or partially collaborative learning behaviors. In this new model, each automaton performs reinforcement learning process in order to identify random environments exchanging information with its adjacent automata. Several computer simulations indicate the availability of the proposed model.

NASH equilibrium; INFORMATION sharing; REINFORCEMENT learning; COLLABORATIVE learning; INFORMATION resources management

Electronics & Communications in Japan, 2018, Vol 101, Issue 3, p28

1942-9533

Academic Journal

10.1002/ecj.12031