Self-play is a technique for machine learning in multi-agent systems where a learning algorithm learns by interacting with copies of itself.

LP-based techniques struggling to scale favorably in large instances.

In this paper, we develop efficient parameterized algorithms for regimes between these two extremes.

As such, in games where the marginalization of CCE coincides with the game's Nash equilibria (like

Reaching consensus and convergence to equilibrium are two major challenges of multi-agent systems. Although each has attracted significant attention, relatively few studies address both challenges at the same time. This paper examines the connection between the notions of consensus and equilibrium in a multi-agent system where multiple interacting sub-populations coexist. We argue that consensus can be seen as an intricate component of intra-population stability, whereas equilibrium can be seen as encoding inter-population stability. We show that smooth fictitious play, a well-known learning model in game theory, can achieve both consensus and convergence to equilibrium in diverse multi-agent settings. Moreover, we show that the consensus formation process plays a crucial role in the seminal thorny problem of equilibrium selection in multi-agent learning.