  Real-world applications of reinforcement learning for recommendation and
experimentation faces a practical challenge: the relative reward of different
bandit arms can evolve over the lifetime of the learning agent. To deal with
these non-stationary cases, the agent must forget some historical knowledge, as
it may no longer be relevant to minimise regret. We present a solution to
handling non-stationarity that is suitable for deployment at scale, to provide
business operators with automated adaptive optimisation. Our solution aims to
provide interpretable learning that can be trusted by humans, whilst responding
to non-stationarity to minimise regret. To this end, we develop an adaptive
Bayesian learning agent that employs a novel form of dynamic memory. It enables
interpretability through statistical hypothesis testing, by targeting a set
point of statistical power when comparing rewards and adjusting its memory
dynamically to achieve this power. By design, the agent is agnostic to
different kinds of non-stationarity. Using numerical simulations, we compare
its performance against an existing proposal and show that, under multiple
non-stationary scenarios, our agent correctly adapts to real changes in the
true rewards. In all bandit solutions, there is an explicit trade-off between
learning and achieving maximal performance. Our solution sits on a different
point on this trade-off when compared to another similarly robust approach: we
prioritise interpretability, which relies on more learning, at the cost of some
regret. We describe the architecture of a large-scale deployment of automatic
optimisation-as-a-service where our agent achieves interpretability whilst
adapting to changing circumstances.
