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This work proposes a policy learning algorithm for seeking generalised feedback Nash equilibria (GFNE) in $N_P$-player noncooperative dynamic games. We consider linear-quadratic games with stochastic dynamics and design a best-response…

最优化与控制 · 数学 2025-06-13 Otacilio B. L. Neto , Michela Mulas , Francesco Corona

In this paper, the problem of finding a generalized Nash equilibrium (GNE) of a networked game is studied. Players are only able to choose their decisions from a feasible action set. The feasible set is considered to be a private linear…

计算机科学与博弈论 · 计算机科学 2017-03-27 Farzad Salehisadaghiani , Lacra Pavel

One of the natural objectives of the field of the social networks is to predict agents' behaviour. To better understand the spread of various products through a social network arXiv:1105.2434 introduced a threshold model, in which the nodes…

计算机科学与博弈论 · 计算机科学 2013-04-09 Sunil Simon , Krzysztof R. Apt

In this paper, we aim to design a distributed approximate algorithm for seeking Nash equilibria of an aggregative game. Due to the local set constraints of each player, projectionbased algorithms have been widely employed for solving such…

最优化与控制 · 数学 2021-08-30 Gehui Xu , Guanpu Chen , Hongsheng Qi , Yiguang Hong

This paper proposes and studies a class of discrete-time finite-time-horizon Stackelberg mean-field games, with one leader and an infinite number of identical and indistinguishable followers. In this game, the objective of the leader is to…

最优化与控制 · 数学 2022-10-11 Xin Guo , Anran Hu , Jiacheng Zhang

In this paper, an aggregate game approach is proposed for the modeling and analysis of energy consumption control in smart grid. Since the electricity user's cost function depends on the aggregate load, which is unknown to the end users, an…

经济学 · 定量金融 2017-03-22 Maojiao Ye , Guoqiang Hu

We study $n$-player turn-based games played on a finite directed graph. For each play, the players have to pay a cost that they want to minimize. Instead of the well-known notion of Nash equilibrium (NE), we focus on the notion of subgame…

计算机科学与博弈论 · 计算机科学 2015-04-13 Thomas Brihaye , Véronique Bruyère , Noémie Meunier , Jean-François Raskin

We develop provably efficient reinforcement learning algorithms for two-player zero-sum finite-horizon Markov games with simultaneous moves. To incorporate function approximation, we consider a family of Markov games where the reward…

机器学习 · 计算机科学 2020-06-25 Qiaomin Xie , Yudong Chen , Zhaoran Wang , Zhuoran Yang

Nash equilibrium is perhaps the best-known solution concept in game theory. Such a solution assigns a strategy to each player which offers no incentive to unilaterally deviate. While a Nash equilibrium is guaranteed to always exist, the…

计算机科学与博弈论 · 计算机科学 2025-04-29 David Sychrovský , Christopher Solinas , Revan MacQueen , Kevin Wang , James R. Wright , Nathan R. Sturtevant , Michael Bowling

In this paper, the problem of finding a Nash equilibrium of a multi-player game is considered. The players are only aware of their own cost functions as well as the action space of all players. We develop a relatively fast algorithm within…

系统与控制 · 计算机科学 2017-05-09 Farzad Salehisadaghiani , Lacra Pavel

Many important real-world settings contain multiple players interacting over an unknown duration with probabilistic state transitions, and are naturally modeled as stochastic games. Prior research on algorithms for stochastic games has…

计算机科学与博弈论 · 计算机科学 2021-02-19 Sam Ganzfried

Generalized Nash equilibrium (GNE) is a solution concept for complete information games, in which each player's objective function and feasible region depend on other players' actions. While numerical methods for finding GNE when players…

最优化与控制 · 数学 2025-12-10 Stuart M. Harwood , Dimitri J. Papageorgiou

While Nash equilibria are guaranteed to exist, they may exhibit dense support, making them difficult to understand and execute in some applications. In this paper, we study $k$-sparse commitments in games where one player is restricted to…

计算机科学与博弈论 · 计算机科学 2025-04-22 Salam Afiouni , Jakub Černý , Chun Kai Ling , Christian Kroer

Today's wireless networks are increasingly crowded with an explosion of wireless users, who have greater and more diverse quality of service (QoS) demands than ever before. However, the amount of spectrum that can be used to satisfy these…

网络与互联网体系结构 · 计算机科学 2013-10-10 Richard Southwell , Xu Chen , Jianwei Huang

There has been significant recent progress in algorithms for approximation of Nash equilibrium in large two-player zero-sum imperfect-information games and exact computation of Nash equilibrium in multiplayer strategic-form games. While…

计算机科学与博弈论 · 计算机科学 2025-10-01 Sam Ganzfried

Noticing that physical limitations are ubiquitous in practical engineering systems, this paper considers Nash equilibrium seeking for games in systems where the control inputs are bounded. More specifically, first-order integrator-type…

最优化与控制 · 数学 2020-09-29 Maojiao Ye

We study turn-based quantitative multiplayer non zero-sum games played on finite graphs with reachability objectives. In such games, each player aims at reaching his own goal set of states as soon as possible. A previous work on this model…

计算机科学与博弈论 · 计算机科学 2019-03-14 Thomas Brihaye , Véronique Bruyère , Julie De Pril , Hugo Gimbert

In this work, we seek a more refined understanding of the complexity of local optimum computation for Max-Cut and pure Nash equilibrium (PNE) computation for congestion games with weighted players and linear latency functions. We show that…

A Nash Equilibrium (NE) is a strategy profile resilient to unilateral deviations, and is predominantly used in the analysis of multiagent systems. A downside of NE is that it is not necessarily stable against deviations by coalitions. Yet,…

计算机科学与博弈论 · 计算机科学 2014-01-16 Michal Feldman , Tami Tamir

We study the sample complexity of identifying the pure strategy Nash equilibrium (PSNE) in a two-player zero-sum matrix game with noise. Formally, we are given a stochastic model where any learner can sample an entry $(i,j)$ of the input…

机器学习 · 计算机科学 2023-11-29 Arnab Maiti , Ross Boczar , Kevin Jamieson , Lillian J. Ratliff