Hyperspectral imaging (HSI) captures a greater level of spec- tral
detail than traditional optical imaging, making it a potentially valu-
able intraoperative tool when precise tissue differentiation is
essential.  Hardware limitations of current optical systems used for
handheld real- time video HSI result in a limited focal depth, thereby
posing usability issues for integration of the technology into the
operating room. This work integrates a focus-tunable liquid lens into
a video HSI exoscope, and proposes novel video autofocusing methods
based on deep reinforce- ment learning. A first-of-its-kind robotic
focal-time scan was performed to create a realistic and reproducible
testing dataset. We benchmarked our proposed autofocus algorithm
against traditional policies, and found our novel approach to perform
significantly (p < 0.05) better than tra- ditional techniques (0.070 ±
.098 mean absolute focal error compared to 0.146 ± .148). In addition,
we performed a blinded usability trial by having two neurosurgeons
compare the system with different autofocus policies, and found our
novel approach to be the most favourable, making our system a
desirable addition for intraoperative HSI.