# Real-time Bidding Strategy in Display Advertising: An Empirical Analysis

arXiv: https://arxiv.org/abs/2212.02222 · HTML: https://mlsanity.com/paper/2212.02222/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [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)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [Real-time Bidding for Online Advertising: Measurement and Analysis](https://mlsanity.com/paper/1306.6542/) (2013)
- [A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising](https://mlsanity.com/paper/2106.06224/) (2021)
- [Real-Time Bidding Benchmarking with iPinYou Dataset](https://mlsanity.com/paper/1407.7073/) (2014)
- [Profit Maximization for Online Advertising Demand-Side Platforms](https://mlsanity.com/paper/1706.01614/) (2017)
- [Statistical Arbitrage Mining for Display Advertising](https://mlsanity.com/paper/1506.03837/) (2015)
- [MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding](https://mlsanity.com/paper/2002.07408/) (2020)

## Built on by (most cited first, 4 of 4)

- [Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization](https://mlsanity.com/paper/2508.02002/) (2025)
- [Breaking Determinism: Stochastic Modeling for Reliable Off-Policy Evaluation in Ad Auctions](https://mlsanity.com/paper/2512.03354/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model](https://mlsanity.com/paper/2302.03332/) (2023)

## Abstract (arXiv)

Bidding strategies that help advertisers determine bidding prices are receiving increasing attention as more and more ad impressions are sold through real-time bidding systems. This paper first describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. Then, several representative bidding strategies are introduced, especially the research advances and challenges of reinforcement learning-based bidding strategies. Further, we quantitatively evaluate the performance of several representative bidding strategies on the iPinYou dataset. Specifically, we examine the effects of state, action, and reward function on the performance of reinforcement learning-based bidding strategies. Finally, we summarize the general steps for optimizing bidding strategies using reinforcement learning algorithms and present our suggestions.