Multi-agent Negotiation in Real-time Bidding
Chao Kong, Haibei Zhu, Hao Li, Jianye Liu, Zheng Wang, Yinliang Qian · 2019
In this paper, we study the problem of multi-agent negotiation in real-time bidding scenario. We present a new solution, Extended Q-learning Network (EQN), which iteratively assigns the state transition probability and finally converges to a unique optimum effectively; Importantly, we propose a naïve edge computing framework between mobile devices and cloud servers to handle the data preprocessing and transmission simultaneously to reduce the load of cloud servers; Finally, extensive experiment on real dataset demonstrates EQN's soundness (fast convergence), properties and effectiveness (achieve state-of-the-art in real-time bidding task). Our experimental results manifest that our proposed approach outperforms the comparable baselines.