Reinforcement learning for intensional data management

We will review how techniques from reinforcement learning (bandits,
Markov decision processes) naturally occur in a wide variety of tasks
related to intensional data management, i.e., management of data whose
access is associated to a cost. This covers a range of applications, from
Web crawling to crowdsourcing, from query optimization to management of
virtual machines. In contrast with traditional reinforcement learning
applications, data management scenarios often involve a very large but
heavily structured state or action space, requiring adaptations of
traditional techniques.