May 2026· American Control Conference· pp. 3633-3638· 1 citation· 30 references
Computer Science
TL;DR
This work proposes a fully distributed continuous-time algorithm for shared linear equality constraints that converges without multiplier exchange and reaches any GNE, reducing communication overhead and improving privacy.
Abstract
Recent machine learning research has increasingly focused on equilibrium analysis in non-cooperative games rather than solely on optimal solutions. Many such problems involve shared constraints and can be formulated as Generalized Nash Equilibrium Problems (GNEPs). For strongly monotone games, existing methods compute consensus-based variational GNEs (v-GNEs) by exchanging Lagrange multipliers. We propose a fully distributed continuous-time algorithm for shared linear equality constraints that converges without multiplier exchange and reaches any GNE, reducing communication overhead and improving privacy. Discrete-time schemes are also provided, and the method is validated on a multi-robot placement task.
This work establishes convergence under sufficient conditions using an input-to-state stability (ISS) framework and removes the need to share multipliers, reducing communication and improving privacy in generalized Nash Equilibrium Problems.
This paper is concerned with distributed generalized Nash equilibrium (GNE) seeking problem for noncooperative monotone games with coupled constraints. Each individual player aims to minimize its local cost function, which depends not only on its own decision, but also on other players’ decisions. By exploiting variati...
This paper proposes a communication-free multi-robot task allocation framework based solely on local observations. In this study, tasks are defined as reaching target locations. Each robot estimates the positions of neighboring robots using a Labeled Multi-Bernoulli (LMB) filter and independently assigns tasks through...
A graph neural network (GNN)‐based framework for scalable multiagent reinforcement learning (RL), where each manipulator is represented as a node in a GNN, and message‐passing edges provide a communication mechanism that enables agents to share information effectively.
Tong Chen, Bo Fu, Dawn M. Tilbury et al.· Advanced Intelligent Systems· 0 citations
This paper investigates the distributed generalized Nash equilibrium (GNE) seeking problem for purely monotone games with set and shared affine inequality constraints over communication-constrained networks. A continuous-time projected dynamical system is developed by combining a regularization technique with a penalty...
Sheng-Li Du, Shuo Li, Tian-Li Xu et al.· IEEE Transactions on Signal...· 0 citations
This work proposes a fast and versatile interior point solver for constrained dynamic games, along with a computationally efficient second-order correction that increases the probability of converging to a local GNE solution in constrained dynamic games.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduSep 29, 2026
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
