Bidding strategies that help advertisers determine bidding prices are receiving increasing attention as more and more ad impressions are sold through real-time bidding systems. This paper first describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. Then, several representative bidding strategies are introduced, especially the research advances and challenges of reinforcement learning-based bidding strategies. Further, we quantitatively evaluate the performance of several representative bidding strategies on the iPinYou dataset. Specifically, we examine the effects of state, action, and reward function on the performance of reinforcement learning-based bidding strategies. Finally, we summarize the general steps for optimizing bidding strategies using reinforcement learning algorithms and present our suggestions.

翻译：帮助广告商确定投标价格的投标战略日益受到重视,因为越来越多的广告印象通过实时投标系统出售,本文件首先说明在实时投标展示广告中为个别广告商优化投标战略的问题和挑战,然后推出若干有代表性的投标战略,特别是加强学习型投标战略的研究进展和挑战。此外,我们还从数量上评价iPinYou数据集的若干有代表性的投标战略的绩效。具体地说,我们审查了国家、行动和奖励职能对加强学习型投标战略绩效的影响。最后,我们总结了利用强化学习算法优化投标战略的一般步骤,并提出了我们的建议。