Title :
Multi-Agent Reinforcement Learning: A Survey
Author :
Busoniu, Lucian ; Babuska, Robert ; De Schutter, Bart
Author_Institution :
Delft Center for Syst. & Control, Delft Univ. of Technol.
Abstract :
Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined
Keywords :
learning (artificial intelligence); multi-agent systems; multiagent learning goal; multiagent reinforcement learning; Collaboration; Control systems; Distributed control; Environmental economics; Feedback; Game theory; Learning; Multiagent systems; Robot kinematics; Telecommunication control; distributed control; game theory; multi-agent systems; reinforcement learning;
Conference_Titel :
Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on
Conference_Location :
Singapore
Print_ISBN :
1-4244-0341-3
Electronic_ISBN :
1-4214-042-1
DOI :
10.1109/ICARCV.2006.345353