MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models

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

Abstract

Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials, a property known as sample efficiency. This research thus investigates the use of learned world models to improve sample efficiency. We present a novel multi-agent model-based RL algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO), utilizing the Centralized Learning for Decentralized Execution (CLDE) framework. CLDE algorithms allow a group of agents to act in a fully decentralized manner after training. This is a desirable property for many systems comprising of multiple robots. MAMBPO uses a learned world model to improve sample efficiency compared to model-free Multi-Agent Soft Actor-Critic (MASAC). We demonstrate this on two simulated multi-robot tasks, where MAMBPO achieves a similar performance to MASAC, but requires far fewer samples to do so. Through this, we take an important step towards making real-life learning for multi-robot systems possible.
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Subtitle of host publicationProceedings
PublisherIEEE
Pages5635-5640
Number of pages6
ISBN (Electronic)978-1-6654-1714-3
ISBN (Print)978-1-6654-1715-0
DOIs
Publication statusPublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Online at Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Country/TerritoryCzech Republic
CityOnline at Prague
Period27/09/211/10/21

Fingerprint

Dive into the research topics of 'MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models'. Together they form a unique fingerprint.

Cite this