Distributed Learning, Estimation and Control in the Routing Game
Walid Krichene

Citation
Walid Krichene. "Distributed Learning, Estimation and Control in the Routing Game". Talk or presentation, 4, November, 2015.

Abstract
Routing games offer a simple yet powerful model of congestion in traffic networks, both in transportation and communication systems. The congestion in such systems is affected by the combined decision of the agents (drivers or routers), so modeling the decision process of the agents is important, not only to estimate and predict the behavior of the system, but also to be able to control it. This decision process is often called learning, as agents "learn" information about the system or about the other agents. We propose and study different models of learning with the following requirement: the joint learning dynamics should converge asymptotically to the Nash equilibrium of the game. In particular, we focus on two important properties: Is the model robust to stochastic perturbations (such as measurement noise)? And does the model allow heterogeneous learning (different agents may follow different learning strategies)? We study these questions using tools from online learning theory and stochastic approximation theory. Then we present a web application that we developed, and which simulates the routing game: Players can connect to the web app and participate in the game, by iteratively making decisions about their routes and observing outcomes. We show preliminary results from data collected from the application. In particular, we propose and solve a model estimation problem to estimate the learning dynamics of the players, and compare the predictions of the model to the actual behavior of the players, and discuss extensions and open questions.

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Citation formats

• HTML

  Walid Krichene. <a
  href="http://www.cps-forces.org/pubs/101.html"
  ><i>Distributed Learning, Estimation and Control in
  the Routing Game</i></a>, Talk or presentation, 
  4, November, 2015.

• Plain text

  Walid Krichene. "Distributed Learning, Estimation and
  Control in the Routing Game". Talk or presentation,  4,
  November, 2015.

• BibTeX

  @presentation{Krichene15_DistributedLearningEstimationControlInRoutingGame,
      author = {Walid Krichene},
      title = {Distributed Learning, Estimation and Control in
                the Routing Game},
      day = {4},
      month = {November},
      year = {2015},
      abstract = {Routing games offer a simple yet powerful model of
                congestion in traffic networks, both in
                transportation and communication systems. The
                congestion in such systems is affected by the
                combined decision of the agents (drivers or
                routers), so modeling the decision process of the
                agents is important, not only to estimate and
                predict the behavior of the system, but also to be
                able to control it. This decision process is often
                called learning, as agents "learn" information
                about the system or about the other agents. We
                propose and study different models of learning
                with the following requirement: the joint learning
                dynamics should converge asymptotically to the
                Nash equilibrium of the game. In particular, we
                focus on two important properties: Is the model
                robust to stochastic perturbations (such as
                measurement noise)? And does the model allow
                heterogeneous learning (different agents may
                follow different learning strategies)? We study
                these questions using tools from online learning
                theory and stochastic approximation theory. Then
                we present a web application that we developed,
                and which simulates the routing game: Players can
                connect to the web app and participate in the
                game, by iteratively making decisions about their
                routes and observing outcomes. We show preliminary
                results from data collected from the application.
                In particular, we propose and solve a model
                estimation problem to estimate the learning
                dynamics of the players, and compare the
                predictions of the model to the actual behavior of
                the players, and discuss extensions and open
                questions.},
      URL = {http://cps-forces.org/pubs/101.html}
  }
  

Posted by Carolyn Winter on 4 Nov 2015.
Groups: forces
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