eprintid: 10070682
rev_number: 16
eprint_status: archive
userid: 608
dir: disk0/10/07/06/82
datestamp: 2019-03-27 12:47:07
lastmod: 2021-10-04 00:26:31
status_changed: 2019-03-27 12:47:07
type: proceedings_section
metadata_visibility: show
creators_name: Zheng, L
creators_name: Yang, J
creators_name: Cai, H
creators_name: Zhang, W
creators_name: Wang, J
creators_name: Yu, Y
title: MAgent: A many-agent reinforcement learning platform for artificial collective intelligence
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Reinforcement learning; multiagent system; learning environment;
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents. Within the interactions among a population of agents, it enables not only the study of learning algorithms for agents' optimal polices, but more importantly, the observation and understanding of individual agent's behaviors and social phenomena emerging from the AI society, including communication languages, leaderships, altruism. MAgent is highly scalable and can host up to one million agents on a single GPU server. MAgent also provides flexible configurations for AI researchers to design their customized environments and agents. In this demo, we present three environments designed on MAgent and show emerged collective intelligence by learning from scratch.
date: 2018-02-07
date_type: published
publisher: AAAI
official_url: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16459
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1629868
isbn_13: 9781577358008
lyricists_name: Wang, Jun
lyricists_id: JWANG00
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
place_of_pub: New Orleans, LA, USA
pagerange: 8222-8223
event_title: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
citation:        Zheng, L;    Yang, J;    Cai, H;    Zhang, W;    Wang, J;    Yu, Y;      (2018)    MAgent: A many-agent reinforcement learning platform for artificial collective intelligence.                     In:   (Proceedings) 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. (pp. pp. 8222-8223).  AAAI: New Orleans, LA, USA.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10070682/1/16459-77215-1-PB.pdf