Reinforcement learning covers a broad category of control problems in which the learning agent interacts with the environment in order to learn to maximize the collected utility. Such exploratory interaction is often costly, encouraging sample-efficient algorithms to be used in…

The construction of accurate predictive models over sequence data is of fundamental importance in the pursuit of developing artificial agents that are capable of (near)-optimal sequential decision-making in disparate environments.If such predictive models are available, they can be exploited by…

This thesis explores the challenges involved in building autonomous exploration and monitoring systems, and makes contributions on four fronts: describing the semantic context of the collected data, summarizing this information, deciding where to collect this data, and making optimal online…