2022/05/29 by Rui Ai, Chang Wang, Ai, Rui +9 · 1 citation
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Grid Energy Management #cs.GT #cs.LG
paper · pdf · doi:10.48550/arxiv.2205.14572
23 pages, 1 figure
arxiv created 2022/05/29 · openalex publication_date 2022/05/29 · arxiv updated 2022/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions. Although there has been a line of works concerning online bidding strategies in first-price auctions, it still remains open how to handle budget constraints in the problem. In the present paper, we initiate the study for a buyer with budgets to learn online bidding strategies in repeated first-price auctions. We propose an RL-based bidding algorithm against the optimal non-anticipating strategy under stationary competition. Our algorithm obtains \widetilde O(√ T)-regret if the bids are all revealed at the end of each round. With the restriction that the buyer only sees the winning bid after each round, our modified algorithm obtains \widetilde O(T(7)/(12))-regret by techniques developed from survival analysis. Our analysis extends to the more general scenario where the buyer has any bounded instantaneous utility function with regrets of the same order.