Scalable Negotiating Agent Strategy via Multi-Issue Policy Network (Student Abstract)
Keywords:Reinforcement Learning, Automated Negotiation, Deep Neural Network, Multi-issue Policy Network
AbstractPrevious 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.
How to Cite
Shimizu, T., Higa, R., Takahashi, T., Fujita, K., & Nakadai, S. (2023). Scalable Negotiating Agent Strategy via Multi-Issue Policy Network (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16326-16327. https://doi.org/10.1609/aaai.v37i13.27023
AAAI Student Abstract and Poster Program