Scalable Negotiating Agent Strategy via Multi-Issue Policy Network (Student Abstract) - Shimizu, Takumu; Higa, Ryota; Takahashi, Toki; Fujita, Katsuhide; Nakadai, Shinji (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
Previous research on the comprehensive negotiation strategy using deep reinforcement learning (RL) has scalability issues of not performing effectively in the large-sized domains. We improve negotiation strategy via deep RL by considering an issue-based represented deep policy network to deal with multi-issue negotiation. The architecture of the proposed learning agent considers the characteristics of multi-issue negotiation domains and policy-based learning. We demonstrate that proposed method achieve equivalent or higher utility than existing negotiation agents in the large-sized domains.