  Value estimation is a critical component of the reinforcement learning (RL)
paradigm. The question of how to effectively learn value predictors from data
is one of the major problems studied by the RL community, and different
approaches exploit structure in the problem domain in different ways. Model
learning can make use of the rich transition structure present in sequences of
observations, but this approach is usually not sensitive to the reward
function. In contrast, model-free methods directly leverage the quantity of
interest from the future, but receive a potentially weak scalar signal (an
estimate of the return). We develop an approach for representation learning in
RL that sits in between these two extremes: we propose to learn what to model
in a way that can directly help value prediction. To this end, we determine
which features of the future trajectory provide useful information to predict
the associated return. This provides tractable prediction targets that are
directly relevant for a task, and can thus accelerate learning the value
function. The idea can be understood as reasoning, in hindsight, about which
aspects of the future observations could help past value prediction. We show
how this can help dramatically even in simple policy evaluation settings. We
then test our approach at scale in challenging domains, including on 57 Atari
2600 games.
