  Most algorithms for the multi-armed bandit problem in reinforcement learning
aimed to maximize the expected reward, which are thus useful in searching the
optimized candidate with the highest reward (function value) for diverse
applications (e.g., AlphaGo). However, in some typical application scenaios
such as drug discovery, the aim is to search a diverse set of candidates with
high reward. Here we propose a reversible upper confidence bound (rUCB)
algorithm for such a purpose, and demonstrate its application in virtual
screening upon intrinsically disordered proteins (IDPs). It is shown that rUCB
greatly reduces the query times while achieving both high accuracy and low
performance loss.The rUCB may have potential application in multipoint
optimization and other reinforcement-learning cases.
