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.

中文翻译：

---

使用深度强化学习协商团队组建

摘要 当自主智能体在同一环境中交互时，它们往往必须合作才能实现目标。代理商有效合作的一种方式是组成一个团队，就联合计划达成具有约束力的协议并执行。然而，当代理人自私时，必须适当分配团队组建的收益以激励达成一致。已经提出了多种用于多代理协商的方法，但通常仅适用于特定的协商协议。更通用的方法通常需要人工输入或特定领域的数据，因此无法扩展。为了解决这个问题，我们提出了一个框架，用于训练代理使用深度强化学习进行谈判和组建团队。重要的是，我们的方法不对特定的协商协议做任何假设，而是完全由经验驱动。我们在非空间和空间扩展的团队形成谈判环境中评估我们的方法，证明我们的代理击败了手工制作的机器人并达到与合作博弈论预测的公平解决方案一致的谈判结果。此外，我们调查代理的物理位置如何影响谈判结果。