Decomposition-Coordination Method for Finite Horizon Bandit Problems

TL;DR

This paper introduces DeCo, a decomposition-coordination heuristic for finite horizon multi-armed bandit problems that significantly reduces computational complexity and nearly matches the optimal solutions, outperforming classic algorithms in simulations.

Contribution

The paper proposes a novel heuristic, DeCo, that decomposes multi-armed bandit problems into parallel single-arm problems, reducing computation and providing a theoretical regret lower bound.

Findings

DeCo reduces computational time to nearly linear in the number of arms.

DeCo nearly matches the optimal solution for two-armed bandits.

DeCo outperforms classic algorithms like Thompson sampling and UCB in simulations.

Abstract

Optimally solving a multi-armed bandit problem suffers the curse of dimensionality. Indeed, resorting to dynamic programming leads to an exponential growth of computing time, as the number of arms and the horizon increase. We introduce a decompositioncoordination heuristic, DeCo, that turns the initial problem into parallelly coordinated one-armed bandit problems. As a consequence, we obtain a computing time which is essentially linear in the number of arms. In addition, the decomposition provides a theoretical lower bound on the regret. For the two-armed bandit case, dynamic programming provides the exact solution, which is almost matched by the DeCo heuristic. Moreover, in numerical simulations with up to 100 rounds and 20 arms, DeCo outperforms classic algorithms (Thompson sampling and Kullback-Leibler upper-confidence bound) and almost matches the theoretical lower bound on the regret for 20 arms.