Multi-agent hierarchical reinforcement learning with dynamic termination

Dongge Han, Wendelin Boehmer, Michael Wooldridge, Alex Rogers

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)

Abstract

In a multi-agent system, an agent's optimal policy will typically depend on the policies of other agents. Predicting the behaviours of others, and responding promptly to changes in such behaviours, is therefore a key issue in multi-agent systems research. One obvious possibility is for each agent to broadcast their current intention, for example, the currently executed option in a hierarchical RL framework. However, this approach results in inflexible agents when options have an extended duration. While adjusting the executed option at each step improves flexibility from a single-agent perspective, frequent changes in options can induce inconsistency between an agent's actual behaviour and its broadcasted intention. In order to balance flexibility and predictability, we propose a dynamic termination Bellman equation that allows the agents to flexibly terminate their options.

Original languageEnglish
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages2006-2008
Number of pages3
ISBN (Electronic)9781510892002
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: 13 May 201917 May 2019

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume4
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
CountryCanada
CityMontreal
Period13/05/1917/05/19

Keywords

  • Hierarchical reinforcement learning
  • Multi-agent learning

Fingerprint

Dive into the research topics of 'Multi-agent hierarchical reinforcement learning with dynamic termination'. Together they form a unique fingerprint.

Cite this