VeNAS: Versatile Negotiating Agent Strategy via Deep Reinforcement Learning (Student Abstract)

Toki Takahashi, Ryota Higa, Katsuhide Fujita, Shinji Nakadai

Abstract: Existing research in the field of automated negotiation considers a negotiation architecture in which some of the negotiation components are designed separately by reinforcement learning (RL), but comprehensive negotiation strategy design has not been achieved. In this study, we formulated an RL model based on a Markov decision process (MDP) for bilateral multi-issue negotiations. We propose a versatile negotiating agent that can effectively learn various negotiation strategies and domains through comprehensive strategies using deep RL. We show that the proposed method can achieve the same or better utility than existing negotiation agents.