  The challenge in controlling stochastic systems in which low-probability
events can set the system on catastrophic trajectories is to develop a robust
ability to respond to such events without significantly compromising the
optimality of the baseline control policy. This paper presents CelluDose, a
stochastic simulation-trained deep reinforcement learning adaptive feedback
control prototype for automated precision drug dosing targeting stochastic and
heterogeneous cell proliferation. Drug resistance can emerge from random and
variable mutations in targeted cell populations; in the absence of an
appropriate dosing policy, emergent resistant subpopulations can proliferate
and lead to treatment failure. Dynamic feedback dosage control holds promise in
combatting this phenomenon, but the application of traditional control
approaches to such systems is fraught with challenges due to the complexity of
cell dynamics, uncertainty in model parameters, and the need in medical
applications for a robust controller that can be trusted to properly handle
unexpected outcomes. Here, training on a sample biological scenario identified
single-drug and combination therapy policies that exhibit a 100% success rate
at suppressing cell proliferation and responding to diverse system
perturbations while establishing low-dose no-event baselines. These policies
were found to be highly robust to variations in a key model parameter subject
to significant uncertainty and unpredictable dynamical changes.
