1,350 research outputs found
Nash and Wardrop equilibria in aggregative games with coupling constraints
We consider the framework of aggregative games, in which the cost function of
each agent depends on his own strategy and on the average population strategy.
As first contribution, we investigate the relations between the concepts of
Nash and Wardrop equilibrium. By exploiting a characterization of the two
equilibria as solutions of variational inequalities, we bound their distance
with a decreasing function of the population size. As second contribution, we
propose two decentralized algorithms that converge to such equilibria and are
capable of coping with constraints coupling the strategies of different agents.
Finally, we study the applications of charging of electric vehicles and of
route choice on a road network.Comment: IEEE Trans. on Automatic Control (Accepted without changes). The
first three authors contributed equall
Genetic algorithm for closed-loop equilibrium of high-order linear-quadratic dynamic games
Cataloged from PDF version of article.In this paper, we implement an adaptive search algorithm, genetic algorithm to derive closed-loop Nash equilibria for linear-quadratic dynamic games. The computation of these equilibria is quite difficult to deal with analytically and numerically. Our strategy is to search over all rime-invariant strategies depending only on the current value of the state. Also provided are some evidences which show the success of the algorithm. (C) 2000 IMACS. Published by Elsevier Science B.V. All rights reserved
Generalized Stochastic Dynamic Aggregative Game for Demand-Side Management in Microgrids with Shared Battery
In this paper, we focus on modeling and analysis of demand-side management in
a microgrid where agents utilize grid energy and a shared battery charged by
renewable energy sources. We model the problem as a generalized stochastic
dynamic aggregative game with chance constraints that capture the effects of
uncertainties in the renewable generation and agents' demands. Computing the
solution of the game is a complex task due to probabilistic and coupling
constraints among the agents through the state of charge of the shared battery.
We investigate the Nash equilibrium of this game under uncertainty considering
both the uniqueness of the solution and the effect of uncertainty on the
solution. Simulation results demonstrate that the presented stochastic method
is superior to deterministic methods
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games
The predominant paradigm in evolutionary game theory and more generally
online learning in games is based on a clear distinction between a population
of dynamic agents that interact given a fixed, static game. In this paper, we
move away from the artificial divide between dynamic agents and static games,
to introduce and analyze a large class of competitive settings where both the
agents and the games they play evolve strategically over time. We focus on
arguably the most archetypal game-theoretic setting -- zero-sum games (as well
as network generalizations) -- and the most studied evolutionary learning
dynamic -- replicator, the continuous-time analogue of multiplicative weights.
Populations of agents compete against each other in a zero-sum competition that
itself evolves adversarially to the current population mixture. Remarkably,
despite the chaotic coevolution of agents and games, we prove that the system
exhibits a number of regularities. First, the system has conservation laws of
an information-theoretic flavor that couple the behavior of all agents and
games. Secondly, the system is Poincar\'{e} recurrent, with effectively all
possible initializations of agents and games lying on recurrent orbits that
come arbitrarily close to their initial conditions infinitely often. Thirdly,
the time-average agent behavior and utility converge to the Nash equilibrium
values of the time-average game. Finally, we provide a polynomial time
algorithm to efficiently predict this time-average behavior for any such
coevolving network game.Comment: To appear in AAAI 202
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