  Return caching is a recent strategy that enables efficient minibatch training
with multistep estimators (e.g. the {\lambda}-return) for deep reinforcement
learning. By precomputing return estimates in sequential batches and then
storing the results in an auxiliary data structure for later sampling, the
average computation spent per estimate can be greatly reduced. Still, the
efficiency of return caching could be improved, particularly with regard to its
large memory usage and repetitive data copies. We propose a new data structure,
the Virtual Replay Cache (VRC), to address these shortcomings. When learning to
play Atari 2600 games, the VRC nearly eliminates DQN({\lambda})'s cache memory
footprint and slightly reduces the total training time on our hardware.
