Learning of Cooperative Behaviour in Robot Populations

Dario Bauso, Dario Bauso, Paul A. Trodden, Sandor M. Veres, Michalis Smyrnakis

Research output: Contribution to conferenceOtherpeer-review

1 Citation (Scopus)


This paper addresses convergence and equilibrium properties of game theoretic learning algorithms in robot populations using simple and broadly applicable reward/cost models of cooperation between robotic agents. New models for robot cooperation are proposed by combining regret based learning methods and network evolution models. Results of mean-field game theory are employed in order to show the asymptotic second moment boundedness in the variation of cooperative behaviour. The behaviour of the proposed models are tested in simulation results, which are based on sample networks and a single lane traffic flow case study.
Original languageEnglish
Number of pages6
Publication statusPublished - 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization

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