  Most theoretically motivated work in the offline reinforcement learning
setting requires precise uncertainty estimates. This requirement restricts the
algorithms derived in that work to the tabular and linear settings where such
estimates exist. In this work, we develop a novel method for incorporating
scalable uncertainty estimates into an offline reinforcement learning algorithm
called deep-SPIBB that extends the SPIBB family of algorithms to environments
with larger state and action spaces. We use recent innovations in uncertainty
estimation from the deep learning community to get more scalable uncertainty
estimates to plug into deep-SPIBB. While these uncertainty estimates do not
allow for the same theoretical guarantees as in the tabular case, we argue that
the SPIBB mechanism for incorporating uncertainty is more robust and flexible
than pessimistic approaches that incorporate the uncertainty as a value
function penalty. We bear this out empirically, showing that deep-SPIBB
outperforms pessimism based approaches with access to the same uncertainty
estimates and performs at least on par with a variety of other strong baselines
across several environments and datasets.
