Multi-Agent Pattern Formation: A Distributed Model-Free Deep Reinforcement Learning Approach

Elhadji Amadou Oury Diallo, Toshiharu Sugawara

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

Abstract

In this paper, we investigate how a large-scale system of independently learning agents can collectively form acceptable two-dimensional patterns (pattern formation) from any initial configuration. We propose a decentralized multi-agent deep reinforcement learning architecture MAPF-DQN (Multi-Agent Pattern Formation DQN) in which a set of independent and distributed agents capture their local visual field and learn how to act so as to collectively form target shapes. Agents exploit their individual networks with a central replay memory and target networks that are used to store and update the representation of the environment as well as learning the dynamics of the other agents. We then show that agents trained on random patterns using MAPF-DQN can organize themselves into very complex shapes in large-scale environments. Our results suggest that the proposed framework achieves zero-shot generalization on most of the environments independently of the depth of view of agents.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
DOIs
Publication statusPublished - 2020 Jul
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 2020 Jul 192020 Jul 24

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Period20/7/1920/7/24

Keywords

  • deep reinforcement learning
  • multi-agent systems
  • pattern formation
  • self-organization
  • swarms

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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