Cooperative Multi-Agent Learning: The State of the Art |
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Authors: | Email author" target="_blank">Liviu?PanaitEmail author Sean?Luke |
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Affiliation: | (1) Department of Computer Science, George Mason University, Fairfax, VA 22030, USA |
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Abstract: | Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve
tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with
the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions
to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process.
We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused
on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to
draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems,
agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with
its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition,
scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of
multi-agent learning resources. |
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Keywords: | multi-agent systems machine learning multi-agent learning cooperation survey |
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