Summary of Auctionnet: a Novel Benchmark For Decision-making in Large-scale Games, by Kefan Su et al.
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
by Kefan Su, Yusen Huo, Zhilin Zhang, Shuai Dou, Chuan Yu, Jian Xu, Zongqing Lu, Bo Zheng
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents AuctionNet, a benchmark for bid decision-making in large-scale ad auctions derived from a real-world online advertising platform. The benchmark consists of an ad auction environment, a pre-generated dataset, and performance evaluations of baseline algorithms. The environment is composed of several modules that replicate the integrity and complexity of real-world ad auctions, including deep generative networks to simulate data and auto-bidding agents trained with different decision-making algorithms. The dataset contains 10 million ad opportunities, 48 diverse auto-bidding agents, and over 500 million auction records. Performance evaluations of baseline algorithms for bid decision-making are also presented. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AuctionNet is a new tool that helps researchers study how to make good decisions in big games online. Currently, it’s hard to find real-world data to test these ideas because ad auctions are complex and sensitive. To solve this problem, the authors created AuctionNet, which includes a fake game environment, some pre-made data, and tests of simple decision-making algorithms. The environment is made up of many parts that work together like real-world ad auctions do. The data has lots of different ads, bidding strategies, and auction records. This will help researchers study how to make better decisions in these big games. |
