Safe Sequential Optimization for Switching Environments
This work addresses safe optimization in dynamic settings, which is incremental as it builds on existing Bayesian optimization and change point detection methods.
The paper tackles the problem of sequential decision-making in time-varying environments with safety constraints, proposing Adaptive-SafeOpt, a policy that combines Bayesian optimization and change point detection to maximize an unknown switching function while ensuring safe decisions with high probability.
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.