Markov decision process routing games

We explore an extension of nonatomic routing games that we call Markov decision process routing games where each agent chooses a transition policy between nodes in a network rather than a path from an origin node to a destination node, i.e. each agent in the population solves a Markov decision proce...

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Bibliographic Details
Published in:2017 ACM IEEE 8th International Conference on Cyber Physical Systems (ICCPS) pp. 273 - 279
Main Authors: Calderone, Dan, Sastry, S. Shankar
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 18.04.2017
Series:ACM Other Conferences
Subjects:
anonymous sequential games
Computing methodologies > Machine learning > Machine learning approaches > Stochastic games
Games
Markov decision processes
Markov processes
mean-field games
Networks > Network algorithms > Control path algorithms > Traffic engineering algorithms
Rats
Routing
routing games
Sociology
Statistics
Stochastic games
Theory of computation > Theory and algorithms for application domains > Algorithmic game theory and mechanism design > Network games
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
routing games
Markov decision processes
anonymous sequential games
mean-field games
stochastic games
ISBN:9781450349659, 145034965X
Online Access:Get full text
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Summary:We explore an extension of nonatomic routing games that we call Markov decision process routing games where each agent chooses a transition policy between nodes in a network rather than a path from an origin node to a destination node, i.e. each agent in the population solves a Markov decision process rather than a shortest path problem. We define the appropriate version of a Wardrop equilibrium as well as a potential function for this game in the finite horizon (total reward) case. This work can be thought of as a routing-game-based formulation of continuous population stochastic games (mean-field games or anonymous sequential games). We apply our model to the problem of ridesharing drivers competing for customers.
ISBN:9781450349659
145034965X
DOI:10.1145/3055004.3055026