Deep reinforcement learning for acceptance strategy in bilateral negotiations

Razeghi, YousefYavuz, OzanAydoğan, Reyhan2021-02-052021-02-0520201300-0632http://hdl.handle.net/10679/7271https://doi.org/10.3906/elk-1907-215This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.enginfo:eu-repo/semantics/openAccessDeep reinforcement learning for acceptance strategy in bilateral negotiationsArticle2841824184000055376560000210.3906/elk-1907-215Deep reinforcement learningAutomated bilateral negotiationAcceptance strategy2-s2.0-85090162413