  Predicting and reasoning about the future lie at the heart of many
time-series questions. For example, goal-conditioned reinforcement learning can
be viewed as learning representations to predict which states are likely to be
visited in the future. While prior methods have used contrastive predictive
coding to model time series data, learning representations that encode
long-term dependencies usually requires large amounts of data. In this paper,
we introduce a temporal difference version of contrastive predictive coding
that stitches together pieces of different time series data to decrease the
amount of data required to learn predictions of future events. We apply this
representation learning method to derive an off-policy algorithm for
goal-conditioned RL. Experiments demonstrate that, compared with prior RL
methods, ours achieves $2 \times$ median improvement in success rates and can
better cope with stochastic environments. In tabular settings, we show that our
method is about $20 \times$ more sample efficient than the successor
representation and $1500 \times$ more sample efficient than the standard (Monte
Carlo) version of contrastive predictive coding.
