1 research outputs found
Continuous-Time Multi-Armed Bandits with Controlled Restarts
Time-constrained decision processes have been ubiquitous in many fundamental
applications in physics, biology and computer science. Recently, restart
strategies have gained significant attention for boosting the efficiency of
time-constrained processes by expediting the completion times. In this work, we
investigate the bandit problem with controlled restarts for time-constrained
decision processes, and develop provably good learning algorithms. In
particular, we consider a bandit setting where each decision takes a random
completion time, and yields a random and correlated reward at the end, with
unknown values at the time of decision. The goal of the decision-maker is to
maximize the expected total reward subject to a time constraint . As an
additional control, we allow the decision-maker to interrupt an ongoing task
and forgo its reward for a potentially more rewarding alternative. For this
problem, we develop efficient online learning algorithms with
and regret in a finite and continuous action space
of restart strategies, respectively. We demonstrate an applicability of our
algorithm by using it to boost the performance of SAT solvers