Definition
Multi-agent learning (MAL) refers to settings in which multiple agents learn simultaneously. Usually defined in a game theoretic setting, specifically in repeated games or stochastic games, the key feature that distinguishes multi-agent learning from single-agent learning is that in the former the learning of one agent impacts the learning of others. As a result, neither the problem definition for multi-agent learning nor the algorithms offered follow in a straightforward way from the single-agent case. In this first of two entries on the subject, we focus on the problem definition.
Background
The topic of multi-agent learning (MAL henceforth) has a long history in game theory, almost as long as the history of game theory itself (another more recent term for the area within game theory is interactive learning). In artificial intelligence (AI), the history of single-agent learning is of course as rich if not richer; one need not look further than this encyclopedia for...
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Notes
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Requisite background in game theory can be obtained from the many introductory texts and most compactly from Leyton-Brown and Shoham (2008). Game theoretic work on multi-agent learning is covered in Fudenberg and Levine (1998) and Young (2004). An expanded discussion of the problems addressed under the header of MAL can be found in Shoham et al. (2007) and the responses to it in Vohra and Wellman (2007). Discussion of MAL algorithms, both traditional and more novel ones, can be found in the above references, as well as in Greenwald and Littman (2007).
Recommended Reading
Fudenberg D, Levine D (1998) The theory of learning in games. MIT, Cambridge
Greenwald A, Littman ML (eds) (2007) Special issue on learning and computational game theory. Mach Learn 67(1–2):3–6
Leyton-Brown K, Shoham Y (2008) Essentials of game theory. Morgan and Claypool, San Rafael
Shoham Y, Powers WR, Grenager T (2007) If multiagent learning is the answer, what is the question? Artif Intell 171(1):365–377. Special issue on foundations of multiagent learning
Vohra R, Wellman MP (eds) (2007) Special issue on foundations of multiagent learning. Artif Intell 171(1)
Young HP (2004) Strategic learning and its limits. Oxford University Press, Oxford
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Shoham, Y., Powers, R. (2017). Multi-agent Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_568
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