  Reinforcement learning approaches have long appealed to the data management
community due to their ability to learn to control dynamic behavior from raw
system performance. Recent successes in combining deep neural networks with
reinforcement learning have sparked significant new interest in this domain.
However, practical solutions remain elusive due to large training data
requirements, algorithmic instability, and lack of standard tools. In this
work, we introduce LIFT, an end-to-end software stack for applying deep
reinforcement learning to data management tasks. While prior work has
frequently explored applications in simulations, LIFT centers on utilizing
human expertise to learn from demonstrations, thus lowering online training
times. We further introduce TensorForce, a TensorFlow library for applied deep
reinforcement learning exposing a unified declarative interface to common RL
algorithms, thus providing a backend to LIFT. We demonstrate the utility of
LIFT in two case studies in database compound indexing and resource management
in stream processing. Results show LIFT controllers initialized from
demonstrations can outperform human baselines and heuristics across latency
metrics and space usage by up to 70%.
