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Real-Time Bidding (RTB) is an important mechanism in modern onlineadvertising systems. Advertisers employ bidding strategies in RTB to optimizetheir advertising effects subject to various financial requirements, especiallythe return-on-investment (ROI) constraint. ROIs change non-monotonically duringthe sequential bidding process, and often induce a see-saw effect betweenconstraint satisfaction and objective optimization. While some existingapproaches show promising results in static or mildly changing ad markets, theyfail to generalize to highly dynamic ad markets with ROI constraints, due totheir inability to adaptively balance constraints and objectives amidstnon-stationarity and partial observability. In this work, we specialize inROI-Constrained Bidding in non-stationary markets. Based on a PartiallyObservable Constrained Markov Decision Process, our method exploits anindicator-augmented reward function free of extra trade-off parameters anddevelops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) frameworkto adaptively control the constraint-objective trade-off in non-stationary admarkets. Extensive experiments on a large-scale industrial dataset with twoproblem settings reveal that CBRL generalizes well in both in-distribution andout-of-distribution data regimes, and enjoys superior learning efficiency andstability.

ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning