Distributed Online Learning Algorithm for Noncooperative Games Over Unbalanced Digraphs

This article investigates constrained online noncooperative games (NGs) of multiagent systems over unbalanced digraphs, where the cost functions of players are time-varying and are gradually revealed to corresponding players only after decisions are made. Moreover, in the problem, the players are su...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 11; s. 15846 - 15856
Hlavní autori: Deng, Zhenhua, Zuo, Xiaolong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2024
Predmet:
Cost function
Costs
Couplings
Distributed algorithms
Games
generalized Nash equilibrium (GNE)
Heuristic algorithms
multiagent systems
noncooperative games (NGs)
online games
Power system dynamics
unbalanced digraphs
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:This article investigates constrained online noncooperative games (NGs) of multiagent systems over unbalanced digraphs, where the cost functions of players are time-varying and are gradually revealed to corresponding players only after decisions are made. Moreover, in the problem, the players are subject to local convex set constraints and time-varying coupling nonlinear inequality constraints. To the best of our knowledge, no result about online games with unbalanced digraphs has been reported, let alone constrained online games. To seek the variational generalized Nash equilibrium (GNE) of the game online, a distributed learning algorithm is proposed based on gradient descent, projection, and primal-dual methods. Under the algorithm, sublinear dynamic regrets and constraint violations are established. Finally, online electricity market games illustrate the algorithm.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3290049