Mean-field Games With Finitely Many Players: Independent Learning And Subjectivity

Abstract

Independent learners are agents that employ single-agent algorithms in multi-agent systems, intentionally ignoring the effect of other strategic agents. This paper studies mean-field games from a decentralized learning perspective, with two primary objectives: (i) to identify structure that can guide algorithm design, and (ii) to understand the emergent behaviour in systems of independent learners. We study a new model of partially observed mean-field games with finitely many players, local action observability, and a general observation channel for partial observations of the global state. Specific observation channels considered include (a) global observability, (b) local and mean-field observability, (c) local and compressed mean-field observability, and (d) only local observability. We establish conditions under which the control problem of a given agent is equivalent to a fully observed MDP, as well as conditions under which the control problem is equivalent only to a POMDP. Build

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