  Many real-world sequential decision-making problems involve critical systems
with financial risks and human-life risks. While several works in the past have
proposed methods that are safe for deployment, they assume that the underlying
problem is stationary. However, many real-world problems of interest exhibit
non-stationarity, and when stakes are high, the cost associated with a false
stationarity assumption may be unacceptable. We take the first steps towards
ensuring safety, with high confidence, for smoothly-varying non-stationary
decision problems. Our proposed method extends a type of safe algorithm, called
a Seldonian algorithm, through a synthesis of model-free reinforcement learning
with time-series analysis. Safety is ensured using sequential hypothesis
testing of a policy's forecasted performance, and confidence intervals are
obtained using wild bootstrap.
