Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning

Mona Alshehri, Napoleon Reyes, Andre Barczak

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

1 Citation (Scopus)

Abstract

This work presents an extension to a graph-based evolutionary algorithm, called Genetic Network Programming with Reinforcement Learning (GNP-RL) to make it more amenable for solving coordinated multi-agent path-planning tasks in dynamic environments. We improve the algorithm's ability to evolve meta-level reasoning strategies in three aspects: genetic composition, search and learning strategies, using optimal search algorithm, constraint conformance and task prioritization techniques.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
EditorsBo An, Amal El Fallah Seghrouchni, Gita Sukthankar
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1744-1746
Number of pages3
ISBN (Electronic)9781450375184
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand
Duration: 9 May 202013 May 2020
Conference number: 19th

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2020-May
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Abbreviated titleAAMAS
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period9/05/2013/05/20

Fingerprint

Dive into the research topics of 'Evolving meta-level reasoning with reinforcement learning and A* for coordinated multi-agent path-planning'. Together they form a unique fingerprint.

Cite this