  Reinforcement Learning (RL) is a computational approach to reward-driven
learning in sequential decision problems. It implements the discovery of
optimal actions by learning from an agent interacting with an environment
rather than from supervised data. We contrast and compare RL with traditional
sequential design, focusing on simulation-based Bayesian sequential design
(BSD). Recently, there has been an increasing interest in RL techniques for
healthcare applications. We introduce two related applications as motivating
examples. In both applications, the sequential nature of the decisions is
restricted to sequential stopping. Rather than a comprehensive survey, the
focus of the discussion is on solutions using standard tools for these two
relatively simple sequential stopping problems. Both problems are inspired by
adaptive clinical trial design. We use examples to explain the terminology and
mathematical background that underlie each framework and map one to the other.
The implementations and results illustrate the many similarities between RL and
BSD. The results motivate the discussion of the potential strengths and
limitations of each approach.
