# Impression Allocation and Policy Search in Display Advertising

arXiv: https://arxiv.org/abs/2203.07073 · HTML: https://mlsanity.com/paper/2203.07073/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Real-time Bidding for Online Advertising: Measurement and Analysis](https://mlsanity.com/paper/1306.6542/) (2013)
- [Sequential Selection of Correlated Ads by POMDPs](https://mlsanity.com/paper/1307.3284/) (2013)
- [A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising](https://mlsanity.com/paper/1405.5189/) (2014)
- [Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising](https://mlsanity.com/paper/1611.07599/) (2016)
- [Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand](https://mlsanity.com/paper/1708.01348/) (2017)
- [Optimal Allocation of Real-Time-Bidding and Direct Campaigns](https://mlsanity.com/paper/2006.07070/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Lightweight MPC Bidding Framework for Brand Auction Ads](https://mlsanity.com/paper/2603.07721/) (2026)
- [HMAF: A Hierarchical Multi-Slot GD-RTB Allocation Framework](https://mlsanity.com/paper/2606.09896/) (2026)

## Abstract (arXiv)

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