A distributed joint-learning and auction algorithm for target assignment

Teymur Sadikhov, Minghui Zhu, Sonia Martínez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents incur cost or reward while traveling on the edges of this graph. Agents do not know the graph or the locations of the targets on it. However, they can obtain local information about these by local sensing and communicating with other agents within a limited range. To solve this problem, we come up with a new distributed algorithm that integrates Q-Learning and a distributed auction. The Q-Learning part helps estimate the assignment benefits calculated by summing up rewards over the graph edges for each agent-target pair, while the auction part takes care of assigning agents to targets in a distributed fashion. The algorithm is shown to terminate with a near-optimal assignment in a finite time. Optimality refers to the assignment benefit maximization, which can depend on a target-agent pair value, and the routing cost of the agent to visit the target.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781424477456
StatePublished - 2010
Event49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, United States
Duration: Dec 15 2010Dec 17 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference49th IEEE Conference on Decision and Control, CDC 2010
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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