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In online display advertising, guaranteed contracts and real-time bidding(RTB) are two major ways to sell impressions for a publisher. For largepublishers, simultaneously selling impressions through both guaranteedcontracts and in-house RTB has become a popular choice. Generally speaking, apublisher needs to derive an impression allocation strategy between guaranteedcontracts and RTB to maximize its overall outcome (e.g., revenue and/orimpression quality). However, deriving the optimal strategy is not a trivialtask, e.g., the strategy should encourage incentive compatibility in RTB andtackle common challenges in real-world applications such as unstable trafficpatterns (e.g., impression volume and bid landscape changing). In this paper,we formulate impression allocation as an auction problem where each guaranteedcontract submits virtual bids for individual impressions. With thisformulation, we derive the optimal bidding functions for the guaranteedcontracts, which result in the optimal impression allocation. In order toaddress the unstable traffic pattern challenge and achieve the optimal overalloutcome, we propose a multi-agent reinforcement learning method to adjust thebids from each guaranteed contract, which is simple, converging efficiently andscalable. The experiments conducted on real-world datasets demonstrate theeffectiveness of our method.

Impression Allocation and Policy Search in Display Advertising