Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in data processing and analytics where many algorithm designs have incorporated heuristics and general rules from human knowledge and experience to improve their effectiveness. Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments it is interacting with than statically designed algorithms. Motivated by this trend, we provide a comprehensive review of recent works focusing on utilizing deep reinforcement learning to improve data processing and analytics. First, we present an introduction to key concepts, theories, and methods in deep reinforcement learning. Next, we discuss deep reinforcement learning deployment on database systems, facilitating data processing and analytics in various aspects, including data organization, scheduling, tuning, and indexing. Then, we survey the application of deep reinforcement learning in data processing and analytics, ranging from data preparation, natural language interface to healthcare, fintech, etc. Finally, we discuss important open challenges and future research directions of using deep reinforcement learning in data processing and analytics.

中文翻译：

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用于数据处理和分析的深度强化学习调查

数据处理和分析是基础和普遍的。算法在数据处理和分析中起着至关重要的作用，其中许多算法设计结合了启发式和人类知识和经验的一般规则，以提高其有效性。最近，强化学习，尤其是深度强化学习 (DRL)，在许多领域得到越来越多的探索和利用，因为与静态设计的算法相比，它可以在与之交互的复杂环境中学习更好的策略。在这一趋势的推动下，我们对最近的工作进行了全面回顾，重点是利用深度强化学习来改进数据处理和分析。首先，我们介绍了深度强化学习中的关键概念、理论和方法。下一个，我们讨论了数据库系统上的深度强化学习部署，在各个方面促进数据处理和分析，包括数据组织、调度、调整和索引。然后，我们调查了深度强化学习在数据处理和分析中的应用，从数据准备、自然语言界面到医疗保健、金融科技等。最后，我们讨论了在数据处理中使用深度强化学习的重要开放挑战和未来研究方向和分析。