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

• URL: http://arxiv.org/abs/2002.07408v2
• Date: Wed, 6 Jul 2022 05:07:10 GMT
• Title: MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
• Authors: Haolin Zhou, Chaoqi Yang, Xiaofeng Gao, Qiong Chen, Gongshen Liu and Guihai Chen
• Abstract summary: We propose a Multi-ecTive Actor-Critics algorithm named MoTiAC for the problem of bidding optimization with various goals. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments.
• Score: 47.555870679348416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a Multi-ObjecTive Actor-Critics algorithm based on reinforcement learning (RL), named MoTiAC, for the problem of bidding optimization with various goals. In MoTiAC, objective-specific agents update the global network asynchronously with different goals and perspectives, leading to a robust bidding policy. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments. In addition, we mathematically prove that our model will converge to Pareto optimality. Finally, experiments on a large-scale real-world commercial dataset from Tencent verify the effectiveness of MoTiAC versus a set of recent approaches

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