  We begin with the hypothesis that a model-free agent whose representations
are predictive of properties of future states (beyond expected rewards) will be
more capable of solving and adapting to new RL problems. To test that
hypothesis, we introduce an objective based on Deep InfoMax (DIM) which trains
the agent to predict the future by maximizing the mutual information between
its internal representation of successive timesteps. We test our approach in
several synthetic settings, where it successfully learns representations that
are predictive of the future. Finally, we augment C51, a strong RL baseline,
with our temporal DIM objective and demonstrate improved performance on a
continual learning task and on the recently introduced Procgen environment.
