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To increase brand awareness, many advertisers conclude contracts withadvertising platforms to purchase traffic and then deliver advertisements totarget audiences. In a whole delivery period, advertisers usually desire acertain impression count for the ads, and they also expect that the deliveryperformance is as good as possible (e.g., obtaining high click-through rate).Advertising platforms employ pacing algorithms to satisfy the demands viaadjusting the selection probabilities to traffic requests in real-time.However, the delivery procedure is also affected by the strategies frompublishers, which cannot be controlled by advertising platforms. Preloading isa widely used strategy for many types of ads (e.g., video ads) to make surethat the response time for displaying after a traffic request is legitimate,which results in delayed impression phenomenon. Traditional pacing algorithmscannot handle the preloading nature well because they rely on immediatefeedback signals, and may fail to guarantee the demands from advertisers.  In this paper, we focus on a new research problem of impression pacing forpreloaded ads, and propose a Reinforcement Learning To Pace framework RLTP. Itlearns a pacing agent that sequentially produces selection probabilities in thewhole delivery period. To jointly optimize the two objectives of impressioncount and delivery performance, RLTP employs tailored reward estimator tosatisfy the guaranteed impression count, penalize the over-delivery andmaximize the traffic value. Experiments on large-scale industrial datasetsverify that RLTP outperforms baseline pacing algorithms by a large margin. Wehave deployed the RLTP framework online to our advertising platform, andresults show that it achieves significant uplift to core metrics includingdelivery completion rate and click-through rate.

RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads