  Credit assignment problems, for example policy evaluation in RL, often
require bootstrapping prediction errors through preceding states \textit{or}
maintaining temporally extended memory traces; solutions which are unfavourable
or implausible for biological networks of neurons. We propose theta sequences
-- chains of neural activity during theta oscillations in the hippocampus,
thought to represent rapid playthroughs of awake behaviour -- as a solution. By
analysing and simulating a model for theta sequences we show they compress
behaviour such that existing but short $\mathsf{O}(10)$ ms neuronal memory
traces are effectively extended allowing for bootstrap-free credit assignment
without long memory traces, equivalent to the use of eligibility traces in
TD($\lambda$).
