In this book we have tried to present reinforcement learning not as a collection of individual methods, but as a coherent set of ideas cutting across methods. Each idea can be viewed as a dimension along which methods vary. The set of such dimensions spans a large space of possible methods. By exploring this space at the level of dimensions we hope to obtain the broadest and most lasting understanding. In this chapter we use the concept of dimensions in method space to recapitulate the view of reinforcement learning we have developed in this book and to identify some of the more important gaps in our coverage of the field.