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.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | MAgent: A many-agent reinforcement learning platform for artificial collective intelligence |
| Event: | 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 |
| ISBN-13: | 9781577358008 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.aaai.org/ocs/index.php/AAAI/AAAI18/pap... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Reinforcement learning; multiagent system; learning environment; |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10070682 |
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