Intelligent sequential decision making is a key challenge in artificial intelligence. The problem, commonly formalized as a Markov Decision Process, is studied in two different research communities: planning and reinforcement learning. Departing from a fundamentally different assumption about the type of access to the environment, both research fields have developed their own solution approaches and conventions. The combination of both fields, known as model-based reinforcement learning, has recently shown state-of-the-art results, for example defeating human experts in classic board games like Chess and Go. Nevertheless, literature lacks an integrated view on 1) the similarities between planning and learning, and 2) the possible combinations of both. This dissertation aims to fill this gap. The first half of the book presents a conceptual answer to both questions. We first present a framework that disentangles the common algorithmic space of both fields, showing that they essentially face the same algorithmic design decisions. Moreover, we also present an overview of the different ways in which planning and learning can be combined in one algorithm. The second half of the dissertation provides experimental illustration of these ideas. We present several new combinations of planning and learning, such as a flexible method to learn stochastic dynamics models with neural networks, an extension of a successful planning-learning algorithm (AlphaZero) to deal with continuous action spaces, and a study of the empirical trade-off between planning and learning. Finally, we also illustrate the commonalities between both fields, by designing a new algorithm in one field based on inspiration from the other field. We conclude the thesis with an outlook for the planning-learning field as a whole. Altogether, the dissertation provides a broad theoretical and empirical view on the combination of planning and learning, which promises to be an important frontier in artificial intelligence research in the coming years. @en