ANOTO: Improving Automated Negotiation Via Offline-to-Online Reinforcement Learning.

Siqi Chen, Jianing Zhao,Kai Zhao,Gerhard Weiss, Fengyun Zhang,Ran Su,Yang Dong, Daqian Li,Kaiyou Lei

International Conference on Autonomous Agents and Multiagent Systems（2024）

Cited 0|Views36

Abstract

Automated negotiation is a crucial component for establishing cooperation and collaboration within multi-agent systems.While reinforcement learning (RL)-based negotiating agents have achieved remarkable success in various scenarios, they still face limitations due to certain assumptions on which they are based.In this work, we proposes a novel approach called ANOTO to improve the negotiating agents' ability via offline-to-online RL.ANOTO enables a negotiating agent (1) to communicate with opponents using an end-to-end strategy that covers all negotiation actions, (2) to learn negotiation strategies from historical offline data without requiring active interactions, and (3) to enhance the optimization process during the online phase, facilitating rapid and stable performance improvements for the learned offline strategies.Experimental results, based on a number of negotiation scenarios and recent winning agents from the Automated Negotiating Agents Competitions (ANAC), are provided.

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【要点】：本文提出了一种名为ANOTO的方法，通过离线到在线强化学习改进自动谈判代理的能力，实现了与对手的端到端沟通和从历史离线数据中学习谈判策略。

【方法】：ANOTO方法结合了离线强化学习和在线优化，使得谈判代理可以在不需要主动交互的情况下，从历史数据中学习谈判策略。

【实验】：研究在多个谈判场景以及Automated Negotiating Agents Competitions (ANAC)中的最新胜出者进行了实验，结果表明ANOTO方法能显著提高谈判代理的表现。