arXiv:2409. 01447v3 Announce Type: replace Abstract: We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-response-based learning algorithms.
By Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman Ozdaglar, Adam Wierman
The paper investigates learning Nash equilibria in partially observable Markov games (POMGs) where agents cannot fully observe the state. By focusing on a subclass with independent state transitions and a Markov potential game structure, the authors propose an independent learning algorithm that allows agents to converge to an approximate Nash equilibrium using only their own observations and actions, without communication. Under a filter stability assumption, finite‑history policies are shown to approximate the POMG sufficiently, enabling a surrogate near‑potential Markov game and yielding quasi‑polynomial sample and computational complexity.
By Philip Jordan, Maryam Kamgarpour
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
By Panayotis Mertikopoulos
arXiv:2610. 01181v1 Announce Type: new Abstract: We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs.
By S. Rasoul Etesami
The paper introduces a method for rational synthesis in stochastic multi‑agent systems with unknown dynamics, where each agent has a probabilistic temporal objective expressed in Probabilistic Linear Temporal Logic on Finite Traces. It formulates per‑agent rational synthesis as finding a strategy profile that satisfies the designated agent’s objective while also constituting a stable solution under chosen equilibrium concepts such as Nash or subgame‑perfect equilibrium. The authors provide conditional convergence guarantees for tabular Nash‑Q learning, certify satisfaction with finite‑sample confidence bounds, and evaluate strategic stability via approximate best‑response checking, validating the approach on eight general‑sum concurrent stochastic games.
By Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli
The paper investigates online learning for multiple strategic agents interacting within a shared linear dynamical system that experiences adversarial disturbances. Each agent aims to minimize its own convex loss while observing the full state, and the study extends the stabilizing-baseline approach from nonstochastic online control to this multi‑agent setting. By employing online gradient descent with memory under state‑only and aggregate‑input feedback, the authors establish sublinear, near‑optimal per‑agent regret bounds and, in the common‑interest case, demonstrate equilibrium‑tracking guarantees via a time‑varying potential game framework.
By Anas Barakat, John Lazarsfeld, Georgios Piliouras, Antonios Varvitsiotis