  Adaptive behavior often requires predicting future events. The theory of
reinforcement learning prescribes what kinds of predictive representations are
useful and how to compute them. This paper integrates these theoretical ideas
with work on cognition and neuroscience. We pay special attention to the
successor representation (SR) and its generalizations, which have been widely
applied both as engineering tools and models of brain function. This
convergence suggests that particular kinds of predictive representations may
function as versatile building blocks of intelligence.
