  In an offline reinforcement learning setting, the safe policy improvement
(SPI) problem aims to improve the performance of a behavior policy according to
which sample data has been generated. State-of-the-art approaches to SPI
require a high number of samples to provide practical probabilistic guarantees
on the improved policy's performance. We present a novel approach to the SPI
problem that provides the means to require less data for such guarantees.
Specifically, to prove the correctness of these guarantees, we devise implicit
transformations on the data set and the underlying environment model that serve
as theoretical foundations to derive tighter improvement bounds for SPI. Our
empirical evaluation, using the well-established SPI with baseline
bootstrapping (SPIBB) algorithm, on standard benchmarks shows that our method
indeed significantly reduces the sample complexity of the SPIBB algorithm.
