Learning problems commonly exhibit an interesting feedback mechanism wherein the
population data reacts to competing decision makers' actions. This paper formulates a new …

We study the multi-agent game within the innovative framework of decision-dependent
games, which establishes a feedback mechanism that population data reacts to agents' …

Achieving convergence of multiple learning agents in general $ N $-player games is
imperative for the development of safe and reliable machine learning (ML) algorithms and …

We develop a flexible stochastic approximation framework for analyzing the long-run
behavior of learning in games (both continuous and finite). The proposed analysis template …

This article considers a distributed Nash equilibrium seeking problem, where the players
only have partial access to other players' actions, such as their neighbors' actions. Thus, the …

Learning problems commonly exhibit an interesting feedback mechanism wherein the
population data reacts to competing decision makers' actions. This paper formulates a new …

Multi-player online games depict the interaction of multiple players with each other over
time. Strongly monotone games are of particular interest since they have benign properties …

Pseudo-games are a natural and well-known generalization of normal-form games, in which
the actions taken by each player affect not only the other players' payoffs, as in games, but …

This paper studies an N-coalition non-cooperative game problem, where the players in the
same coalition cooperatively minimize the sum of their local cost functions under a directed …

The influential work of Bravo et al.[1] shows that derivative free gradient play in strongly
monotone games has complexity O (d 2/ε 3), where ε is the target accuracy on the expected …