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MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

18 February 2020
Haolin Zhou
Chaoqi Yang
Xiaofeng Gao
Qiong Chen
Gongshen Liu
Guihai Chen
Guihai Chen
ArXiv (abs)PDFHTML
Abstract

Online real-time bidding (RTB) is known as a complex auction game where ad platforms seek to consider various influential key performance indicators (KPIs), like revenue and return on investment (ROI). The trade-off among these competing goals needs to be balanced on a massive scale. To address the problem, we propose a multi-objective reinforcement learning algorithm, named MoTiAC, for the problem of bidding optimization with various goals. Specifically, in MoTiAC, instead of using a fixed and linear combination of multiple objectives, we compute adaptive weights overtime on the basis of how well the current state agrees with the agent's prior. In addition, we provide interesting properties of model updating and further prove that Pareto optimality could be guaranteed. We demonstrate the effectiveness of our method on a real-world commercial dataset. Experiments show that the model outperforms all state-of-the-art baselines.

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