Online auction scenarios, such as bidding searches on advertising platforms, often require bidders to participate repeatedly in auctions for the same or similar items. We design an algorithm for adaptive automatic bidding in repeated auctions in which the seller and other bidders also update their strategies. We apply and improve the opponent modeling algorithm to allow bidders to learn optimal bidding strategies in this multiagent reinforcement learning environment. The algorithm uses almost no private information about the opponent or restrictions on the strategy space, so it can be extended to multiple scenarios. Our algorithm improves the utility compared to both static bidding strategies and dynamic learning strategies. We hope the application of opponent modeling in auctions will promote the research of automatic bidding strategies in online auctions and the design of non-incentive compatible auction mechanisms.

翻译：在线拍卖场景，例如广告平台上的竞价搜索，通常要求竞拍者针对相同或相似物品重复参与拍卖。我们设计了一种在重复拍卖中自适应自动出价的算法，其中卖方及其他竞拍者也在更新其策略。我们应用并改进了对手建模算法，使竞拍者能够在该多智能体强化学习环境中学习最优出价策略。该算法几乎不依赖于对手的私有信息或对策略空间的限制，因此可推广至多种场景。相比静态出价策略和动态学习策略，我们的算法提升了效用。我们期望对手建模在拍卖中的应用能够促进在线拍卖自动出价策略的研究以及非激励相容拍卖机制的设计。