  Recent work has demonstrated both benefits and limitations from using
supervised approaches (without temporal-difference learning) for offline
reinforcement learning. While off-policy reinforcement learning provides a
promising approach for improving performance beyond supervised approaches, we
observe that training is often inefficient and unstable due to temporal
difference bootstrapping. In this paper we propose a best-of-both approach by
first learning the behavior policy and critic with supervised learning, before
improving with off-policy reinforcement learning. Specifically, we demonstrate
improved efficiency by pre-training with a supervised Monte-Carlo value-error,
making use of commonly neglected downstream information from the provided
offline trajectories. We find that we are able to more than halve the training
time of the considered offline algorithms on standard benchmarks, and
surprisingly also achieve greater stability. We further build on the importance
of having consistent policy and value functions to propose novel hybrid
algorithms, TD3+BC+CQL and EDAC+BC, that regularize both the actor and the
critic towards the behavior policy. This helps to more reliably improve on the
behavior policy when learning from limited human demonstrations. Code is
available at https://github.com/AdamJelley/EfficientOfflineRL
