  Our understanding of reinforcement learning (RL) has been shaped by
theoretical and empirical results that were obtained decades ago using tabular
representations and linear function approximators. These results suggest that
RL methods that use temporal differencing (TD) are superior to direct Monte
Carlo estimation (MC). How do these results hold up in deep RL, which deals
with perceptually complex environments and deep nonlinear models? In this
paper, we re-examine the role of TD in modern deep RL, using specially designed
environments that control for specific factors that affect performance, such as
reward sparsity, reward delay, and the perceptual complexity of the task. When
comparing TD with infinite-horizon MC, we are able to reproduce classic results
in modern settings. Yet we also find that finite-horizon MC is not inferior to
TD, even when rewards are sparse or delayed. This makes MC a viable alternative
to TD in deep RL.
