ROI Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning

• URL: http://arxiv.org/abs/2206.05240v2
• Date: Tue, 14 Jun 2022 08:55:21 GMT
• Title: ROI Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
• Authors: Haozhe Wang, Chao Du, Panyan Fang, Shuo Yuan, Xuming He, Liang Wang, Bo Zheng
• Abstract summary: We specialize in ROI-Constrained Bidding in non-stationary markets. Based on a Partially Observable Constrained Markov Decision Process, we propose the first hard barrier solution to accommodate non-monotonic constraints. Our method exploits a parameter-free indicator-augmented reward function and develops a Curriculum-Guided Bayesian Reinforcement Learning framework.
• Score: 34.82004227655201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, among which a widely adopted one is the return-on-investment (ROI) constraint. ROIs change non-monotonically during the sequential bidding process, usually presenting a see-saw effect between constraint satisfaction and objective optimization. Existing solutions to the constraint-objective trade-off are typically established in static or mildly changing markets. However, these methods fail significantly in non-stationary advertising markets due to their inability to adapt to varying dynamics and partial observability. In this work, we specialize in ROI-Constrained Bidding in non-stationary markets. Based on a Partially Observable Constrained Markov Decision Process, we propose the first hard barrier solution to accommodate non-monotonic constraints. Our method exploits a parameter-free indicator-augmented reward function and develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework to adaptively control the constraint-objective trade-off in non-stationary advertising markets. Extensive experiments on a large-scale industrial dataset with two problem settings reveal that CBRL generalizes well in both in-distribution and out-of-distribution data regimes, and enjoys outstanding stability.

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