# Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms

arXiv: https://arxiv.org/abs/2510.19357 · HTML: https://mlsanity.com/paper/2510.19357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 68 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 30 of 37)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [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)
- [Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2005.06800/) (2020)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Deep Reinforcement Learning for Sponsored Search Real-time Bidding](https://mlsanity.com/paper/1803.00259/) (2018)
- [Sustainable Online Reinforcement Learning for Auto-bidding](https://mlsanity.com/paper/2210.07006/) (2022)
- [AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games](https://mlsanity.com/paper/2412.10798/) (2024)
- [Auto-bidding and Auctions in Online Advertising: A Survey](https://mlsanity.com/paper/2408.07685/) (2024)
- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising](https://mlsanity.com/paper/2106.06224/) (2021)
- [Robust Auction Design in the Auto-bidding World](https://mlsanity.com/paper/2111.02468/) (2021)
- [Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics](https://mlsanity.com/paper/2301.13306/) (2023)
- [Smart Pacing for Effective Online Ad Campaign Optimization](https://mlsanity.com/paper/1506.05851/) (2015)
- [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://mlsanity.com/paper/2107.06650/) (2021)
- [Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising](https://mlsanity.com/paper/1305.3011/) (2013)
- [ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning](https://mlsanity.com/paper/2206.05240/) (2022)
- [Bid Shading in The Brave New World of First-Price Auctions](https://mlsanity.com/paper/2009.01360/) (2020)
- [AiAds: Automated and Intelligent Advertising System for Sponsored Search](https://mlsanity.com/paper/1907.12118/) (2019)
- [Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search](https://mlsanity.com/paper/2106.04075/) (2021)
- [Statistical Arbitrage Mining for Display Advertising](https://mlsanity.com/paper/1506.03837/) (2015)
- [Non-quasi-linear Agents in Quasi-linear Mechanisms](https://mlsanity.com/paper/2012.02893/) (2020)
- [Optimal Real-Time Bidding Frameworks Discussion](https://mlsanity.com/paper/1602.01007/) (2016)
- [Managing Risk of Bidding in Display Advertising](https://mlsanity.com/paper/1701.02433/) (2017)
- [Real-time Bidding Strategy in Display Advertising: An Empirical Analysis](https://mlsanity.com/paper/2212.02222/) (2022)
- [Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets](https://mlsanity.com/paper/2306.07709/) (2023)

## Built on by (most cited first, 1 of 1)

- [Uncertainty Quantification of Click and Conversion Estimates for the Autobidding](https://mlsanity.com/paper/2603.01825/) (2026)

## Abstract (arXiv)

Advertisement auctions play a crucial role in revenue generation for e-commerce companies. To make the bidding procedure scalable to thousands of auctions, the automatic bidding (autobidding) algorithms are actively developed in the industry. Therefore, the fair and reproducible evaluation of autobidding algorithms is an important problem. We present a standardized and transparent evaluation protocol for comparing classical and reinforcement learning (RL) autobidding algorithms. We consider the most efficient autobidding algorithms from different classes, e.g., ones based on the controllers, RL, optimal formulas, etc., and benchmark them in the bidding environment. We utilize the most recent open-source environment developed in the industry, which accurately emulates the bidding process. Our work demonstrates the most promising use cases for the considered autobidding algorithms, highlights their surprising drawbacks, and evaluates them according to multiple metrics. We select the evaluation metrics that illustrate the performance of the autobidding algorithms, the corresponding costs, and track the budget pacing. Such a choice of metrics makes our results applicable to the broad range of platforms where autobidding is effective. The presented comparison results help practitioners to evaluate the candidate autobidding algorithms from different perspectives and select ones that are efficient according to their companies' targets.