Multi-agent learning

Multi-agent learning is the use of machine learning in a multi-agent system.^[1] Typically, agents improve their decisions via experience. In particular, an agent has to learn how to coordinate with the other agents.

Overview[]

According to an article by Shoham et al. in 2007, it is difficult to pinpoint all relevant articles in the domain.^[2] There are some inherent difficulties about multi-agent deep reinforcement learning.^[3] The environment is not stationary anymore, thus the Markov property is violated: transitions and rewards does not only depend on the current state of an agent.

References[]

^ Albrecht, Stefano; Stone, Peter (2017), "Multiagent Learning: Foundations and Recent Trends. Tutorial", IJCAI-17 conference (PDF)
^ Shoham, Yoav; Powers, Rob; Grenager, Trond (2007-05-01). "If multi-agent learning is the answer, what is the question?" (PDF). Artificial Intelligence. Foundations of Multi-Agent Learning. 171 (7): 365–377. doi:10.1016/j.artint.2006.02.006. ISSN 0004-3702.
^ Hernandez-Leal, Pablo; Kartal, Bilal; Taylor, Matthew E. (2019-11-01). "A survey and critique of multiagent deep reinforcement learning". Autonomous Agents and Multi-Agent Systems. 33 (6): 750–797. arXiv:1810.05587. doi:10.1007/s10458-019-09421-1. ISSN 1573-7454. S2CID 52981002.

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[1] Albrecht, Stefano; Stone, Peter (2017), "Multiagent Learning: Foundations and Recent Trends. Tutorial", IJCAI-17 conference (PDF)

[2] Shoham, Yoav; Powers, Rob; Grenager, Trond (2007-05-01). "If multi-agent learning is the answer, what is the question?" (PDF). Artificial Intelligence. Foundations of Multi-Agent Learning. 171 (7): 365–377. doi:10.1016/j.artint.2006.02.006. ISSN 0004-3702.

[3] Hernandez-Leal, Pablo; Kartal, Bilal; Taylor, Matthew E. (2019-11-01). "A survey and critique of multiagent deep reinforcement learning". Autonomous Agents and Multi-Agent Systems. 33 (6): 750–797. arXiv:1810.05587. doi:10.1007/s10458-019-09421-1. ISSN 1573-7454. S2CID 52981002.

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