UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Negotiating team formation using deep reinforcement learning

Bachrach, Y; Everett, R; Hughes, E; Lazaridou, A; Leibo, JZ; Lanctot, M; Johanson, M; ... Graepel, T; + view all (2020) Negotiating team formation using deep reinforcement learning. Artificial Intelligence , 288 , Article 103356. 10.1016/j.artint.2020.103356. Green open access

[thumbnail of RLNego_AIJ_CR.pdf]
RLNego_AIJ_CR.pdf - Accepted Version

Download (891kB) | Preview


When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.

Type: Article
Title: Negotiating team formation using deep reinforcement learning
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.artint.2020.103356
Publisher version: https://doi.org/10.1016/j.artint.2020.103356
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Multi-agent systems, Team formation, Coalition formation, Reinforcement learning, Deep learning, Cooperative games, Shapley value
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/10109604
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item