Negotiating Team Formation Using Deep Reinforcement Learning

Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Leibo, Marc Lanctot, Mike Johanson, Wojtek Czarnecki, Thore Graepel

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.

Keywords: Reinforcement Learning, Negotiation, Team Formation, Cooperative Game Theory, Shapley Value

TL;DR: Reinforcement learning can be used to train agents to negotiate team formation across many negotiation protocols