  We consider the problem of designing a sequential decision making agent to
maximize an unknown time-varying function which switches with time. At each
step, the agent receives an observation of the function's value at a point
decided by the agent. The observation could be corrupted by noise. The agent is
also constrained to take safe decisions with high probability, i.e., the chosen
points should have a function value greater than a threshold. For this
switching environment, we propose a policy called Adaptive-SafeOpt and evaluate
its performance via simulations. The policy incorporates Bayesian optimization
and change point detection for the safe sequential optimization problem. We
observe that a major challenge in adapting to the switching change is to
identify safe decisions when the change point is detected and prevent
attraction to local optima.
