Decentralized Equilibrium Seeking of Joint Routing and Destination Planning of Electric Vehicles: A Constrained Aggregative Game Approach

Bakhshayesh, Babak Ghaffarzadeh; Kebriaei, Hamed

doi:10.1109/tits.2021.3123207

Cited by 21 publications

(27 citation statements)

References 36 publications

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“…In [6], the authors model the traffic routing problem as multiple coupled Markov Decision Processes (MDPs). This idea is further elaborated in [8], where the authors cast the problem as a generalized aggregative game. In this setting, the infrastructural limits of the network introduce shared constraints between the agent strategies (generalized game) and the cost term coupling the agents depends on an aggregation of all the agents' strategies, namely the network congestion (aggregative game).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Game-Theoretic Traffic Routing

Benenati¹,

Grammatico²

2023

Preprint

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We examine the routing problem for self-interested vehicles using stochastic decision strategies. By approximating the road latency functions and a non-linear variable transformation, we frame the problem as an aggregative game. We characterize the approximation error and we derive a new monotonicity condition for a broad category of games that encompasses the problem under consideration. Next, we propose a semidecentralized algorithm to calculate the routing as a variational generalized Nash equilibrium and demonstrate the solution's benefits with numerical simulations. We also explore a recursive receding-horizon formulation of the routing problem for potential games, showing asymptotic convergence to destinations and analysing closed-loop performance dependence on horizon length through numerical simulations.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Game-Theoretic Traffic Routing

Benenati¹,

Grammatico²

2023

Preprint

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“…Motivation: Numerous engineering systems of recent interest, such as smart electrical grids [1], [2], traffic control systems [3], and wireless communication systems [4]- [6] can be modelled as a generalized game, that is, a system of multiple agents aiming at optimizing their individual, interdependent objectives, while satisfying some common constraints. A typical operating point for these systems is the Generalized Nash Equilibrium (GNE), where no agent can unilaterally improve their objective function [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal selection and tracking of generalized Nash equilibria in monotone games

Benenati¹,

Ananduta²,

Grammatico³

2022

Preprint

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A fundamental open problem in monotone game theory is the computation of a specific generalized Nash equilibrium (GNE) among all the available ones, e.g. the optimal equilibrium with respect to a system-level objective. The existing GNE seeking algorithms have in fact convergence guarantees toward an arbitrary, possibly inefficient, equilibrium. In this paper, we solve this open problem by leveraging results from fixed-point selection theory and in turn derive distributed algorithms for the computation of an optimal GNE in monotone games. We then extend the technical results to the time-varying setting and propose an algorithm that tracks the sequence of optimal equilibria up to an asymptotic error, whose bound depends on the local computational capabilities of the agents.

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“…In particular, if the agents are coupled not only through their respective objective functions, but also through a shared constraint set, then we label the setting as a generalized game. Application examples for generalized games include traffic routing [1], peer-to-peer energy markets [2], [3] and cognitive radio networks [4]. A typical solution paradigm is the generalized Nash equilibrium (GNE), that is, an optimal situation for each agent given the decisions of the remaining agents, and especially the sub-class of variational GNEs (v-GNES), which has recently received widespread attention due to its stability properties [5].…”

Section: Introductionmentioning

confidence: 99%

On the optimal selection of generalized Nash equilibria in linearly coupled aggregative games

Benenati¹,

Ananduta²,

Grammatico³

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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To optimally select a generalized Nash equilibrium, in this paper, we propose a semi-decentralized algorithm based on a double-layer Tikhonov regularization method. Technically, we extend the Tikhonov method for equilibrium selection in non-generalized games to the generalized case by coupling it with the preconditioned forward-backward splitting, which guarantees linear convergence to the solutions of the inner layer problem and allows for a semi-decentralized implementation. We then establish a conceptual connection and draw a comparison between the proposed algorithm and the hybrid steepest descent method, the other known distributed framework for solving the selection problem.

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Decentralized Equilibrium Seeking of Joint Routing and Destination Planning of Electric Vehicles: A Constrained Aggregative Game Approach

Cited by 21 publications

References 36 publications

Probabilistic Game-Theoretic Traffic Routing

Probabilistic Game-Theoretic Traffic Routing

Optimal selection and tracking of generalized Nash equilibria in monotone games

On the optimal selection of generalized Nash equilibria in linearly coupled aggregative games

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