Learning to Coordinate without Sharing Information

Researchers in the eld of Distributed Arti cial Intelligence (DAI) have been developing e cient mechanisms to coordinate the activities of multiple autonomous agents. The need for coordination arises because agents have to share resources and expertise required to achieve their goals. Previous work in the area includes using sophisticated information exchange protocols, investigating heuristics for negotiation, and developing formal models of possibilities of con ict and cooperation among agent interests. In order to handle the changing requirements of continuous and dynamic environments, we propose learning as a means to provide additional possibilities for e ective coordination. We use reinforcement learning techniques on a block pushing problem to show that agents can learn complimentary policies to follow a desired path without any knowledge about each other. We theoretically analyze and experimentally verify the e ects of learning rate on system convergence, and demonstrate bene ...

• Type: Conference
• Year: 1994
• Where: AAAI
• Authors: Sandip Sen, Mahendra Sekaran, John Hale
• Keywords: AAAI 1994, Arti Cial Intelligence, Coordination Arises, Intelligent Agents, Reinforcement Learning