Research progress of multi-agent learning in games
The new wave of artificial intelligence brought about by deep learning and reinforcement learning provides an"end-to-end"solution for agents from perception input to action decision-making output.Multi-agent learning is a frontier subject in the field of intelligent game confrontation,and it faces many problems and challenges such as adversarial environments,non-stationary opponents,incomplete information and uncertain actions.This paper starts from the perspective of game theory,firstly gives the organization of multi-agent learning system,gives an overview of multi-agent learning,and briefly introduces the classification of various multi-agent learning research methods.Secondly,based on the multi-agent learning framework in games,it introduces the basic multi-agent game and meta-game models,game solution concepts and game dynamics,as well as challenges such as diverse learning objectives,non-stationary environment(opponent),and equilibrium hard to compute and easy to transfer.Then comprehensively sort out the multi-agent game strategy learning methods,offline game strategy learning methods and online game strategy learning methods.Finally,some frontiers of multi-agent learning are discussed from three aspects of agent cognitive behavior modelling and collaboration,general game strategy learning methods,and distributed game strategy learning framework.
learning in gamesmulti-agent learningmeta-gameonline no regret learning
万方数据
维普
