  This paper motivates and develops source traces for temporal difference (TD)
learning in the tabular setting. Source traces are like eligibility traces, but
model potential histories rather than immediate ones. This allows TD errors to
be propagated to potential causal states and leads to faster generalization.
Source traces can be thought of as the model-based, backward view of successor
representations (SR), and share many of the same benefits. This view, however,
suggests several new ideas. First, a TD($\lambda$)-like source learning
algorithm is proposed and its convergence is proven. Then, a novel algorithm
for learning the source map (or SR matrix) is developed and shown to outperform
the previous algorithm. Finally, various approaches to using the source/SR
model are explored, and it is shown that source traces can be effectively
combined with other model-based methods like Dyna and experience replay.
