This tutorial presents a broad overview of the field of model-based reinforcement learning (MBRL), with a particular emphasis on deep methods. MBRL methods utilize a model of the environment to make decisions—as opposed to treating the environment as a black box—and present unique opportunities and challenges beyond model-free RL. We discuss methods for learning transition and reward models, ways in which those models can effectively be used to make better decisions, and the relationship between planning and learning. We also highlight ways that models of the world can be leveraged beyond the typical RL setting, and what insights might be drawn from human cognition when designing future MBRL systems.