# AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

arXiv: https://arxiv.org/abs/2412.10798 · HTML: https://mlsanity.com/paper/2412.10798/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising](https://mlsanity.com/paper/2006.16312/) (2020)
- [Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions](https://mlsanity.com/paper/2211.06358/) (2022)
- [Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding](https://mlsanity.com/paper/2402.15102/) (2024)
- [AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing Optimization](https://mlsanity.com/paper/2306.11971/) (2023)

## Built on by (most cited first, 28 of 28)

- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders](https://mlsanity.com/paper/2507.09083/) (2025)
- [GAS: Generative Auto-bidding with Post-training Search](https://mlsanity.com/paper/2412.17018/) (2024)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [DARA: Few-shot Budget Allocation in Online Advertising via In-Context Decision Making with RL-Finetuned LLMs](https://mlsanity.com/paper/2601.14711/) (2026)
- [Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)](https://mlsanity.com/paper/2412.11434/) (2024)
- [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)
- [Multi-agent Auto-Bidding with Latent Graph Diffusion Models](https://mlsanity.com/paper/2503.05805/) (2025)
- [Constant-Memory Strategies in Stochastic Games: Best Responses and Equilibria](https://mlsanity.com/paper/2505.07008/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding](https://mlsanity.com/paper/2601.20257/) (2026)
- [PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding](https://mlsanity.com/paper/2602.08261/) (2026)
- [PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective](https://mlsanity.com/paper/2607.27265/) (2026)
- [Large-Scale Auto-bidding with Nash Equilibrium Constraints](https://mlsanity.com/paper/2503.10304/) (2025)
- [HALO: Hindsight-Augmented Learning for Online Auto-Bidding](https://mlsanity.com/paper/2508.03267/) (2025)
- [Auto-bidding under Return-on-Spend Constraints with Uncertainty Quantification](https://mlsanity.com/paper/2509.16324/) (2025)
- [VAO: Validation-Aligned Optimization for Cross-Task Generative Auto-Bidding](https://mlsanity.com/paper/2510.07760/) (2025)
- [Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies](https://mlsanity.com/paper/2601.02754/) (2026)
- [DecisionLLM: Large Language Models for Long Sequence Decision Exploration](https://mlsanity.com/paper/2601.10148/) (2026)
- [SEGB: Self-Evolved Generative Bidding with Local Autoregressive Diffusion](https://mlsanity.com/paper/2602.22226/) (2025)
- [Knowledge-informed Bidding with Dual-process Control for Online Advertising](https://mlsanity.com/paper/2603.04920/) (2026)
- [JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing](https://mlsanity.com/paper/2604.05845/) (2026)
- [Generative Auto-Bidding with Unified Modeling and Exploration](https://mlsanity.com/paper/2605.19457/) (2026)
- [Constrained Auto-Bidding via Generative Response Modeling](https://mlsanity.com/paper/2605.27811/) (2026)
- [DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation](https://mlsanity.com/paper/2606.14192/) (2026)
- [Beyond Single-Episode Optimization: Sliding-Window Aware Generative Auto-Bidding for Long-Term Advertising Effectiveness](https://mlsanity.com/paper/2607.25233/) (2026)
- [AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System](https://mlsanity.com/paper/2610.19454/) (2026)

## Abstract (arXiv)

Decision-making in large-scale games is an essential research area in artificial intelligence (AI) with significant real-world impact. However, the limited access to realistic large-scale game environments has hindered research progress in this area. In this paper, we present AuctionNet, a benchmark for bid decision-making in large-scale ad auctions derived from a real-world online advertising platform. AuctionNet is composed of three parts: an ad auction environment, a pre-generated dataset based on the environment, and performance evaluations of several baseline bid decision-making algorithms. More specifically, the environment effectively replicates the integrity and complexity of real-world ad auctions through the interaction of several modules: the ad opportunity generation module employs deep generative networks to bridge the gap between simulated and real-world data while mitigating the risk of sensitive data exposure; the bidding module implements diverse auto-bidding agents trained with different decision-making algorithms; and the auction module is anchored in the classic Generalized Second Price (GSP) auction but also allows for customization of auction mechanisms as needed. To facilitate research and provide insights into the environment, we have also pre-generated a substantial dataset based on the environment. The dataset contains 10 million ad opportunities, 48 diverse auto-bidding agents, and over 500 million auction records. Performance evaluations of baseline algorithms such as linear programming, reinforcement learning, and generative models for bid decision-making are also presented as a part of AuctionNet. We believe that AuctionNet is applicable not only to research on bid decision-making in ad auctions but also to the general area of decision-making in large-scale games.