Auto-Bidding in Large-Scale Auctions: Learning Decision-Making in Uncertain and Competitive Games

Decision-making in large-scale games is an essential research area in artificial intelligence with significant real-world impact. An agent confronts the critical task of making high-frequency strategic decisions in an uncertain and competitive environment, characterized by significant randomness and rapidly changing strategies from massive competitors. However, the shortage of large-scale, realistic game systems and datasets has hindered research progress in this area. To provide opportunities for in-depth research on this highly valuable problem, we present the Auto-Bidding in Large-Scale Auctions challenge derived from online advertising, a booming \$626.8 billion industry in 2023. We have developed a standardized ad auction system for the competition, which reproduces the characteristics of real-world large-scale games and incorporates essential features that deserve research attention. We also provide a training framework with a 500-million-record dataset and several industry-proven methods as baselines to help participants quickly start and deeply optimize their strategies.Furthermore, we have prepared a comprehensive promotional strategy, raised sufficient funds, and offered varied incentives to attract more participants from diverse backgrounds.We believe that the proposed competition will provide opportunities for more researchers to gain insights and conduct research in this field, driving technical innovation for both research and real-world practical applications.