  Many healthcare decisions involve navigating through a multitude of treatment
options in a sequential and iterative manner to find an optimal treatment
pathway with the goal of an optimal patient outcome. Such optimization problems
may be amenable to reinforcement learning. A reinforcement learning agent could
be trained to provide treatment recommendations for physicians, acting as a
decision support tool. However, a number of difficulties arise when using RL
beyond benchmark environments, such as specifying the reward function, choosing
an appropriate state representation and evaluating the learned policy.
