A distributed joint-learning and auction algorithm for target assignment

We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents do not know this 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 auctions. The Q-Learning part helps estimate the assignment benefits for each agent-target pair, while the auction part takes care of assigning agents to targets in a distributed and almost optimal fashion. The algorithms are shown to terminate with a near-optimal assignment in a finite time.

• Type: Journal
• Year: 2010
• Where: CDC
• Authors: Teymur Sadikhov, Minghui Zhu, Sonia Martínez
• Keywords: Agent-target Assignment Problem, CDC 2010, Control Systems, Near-optimal Assignment, Undirected Graph