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.
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Abstract
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 |
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