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RESEARCH PRODUCT

Arm Space Decomposition as a Strategy for Tackling Large Scale Multi-armed Bandit Problems

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Recent multi-armed bandit based optimization schemes provide near-optimal balancing of arm exploration against arm exploitation, allowing the optimal arm to be identified with probability arbitrarily close to unity. However, the convergence speed drops dramatically as the number of bandit arms grows large, simply because singling out the optimal arm requires experimentation with all of the available arms. Furthermore, effective exploration and exploitation typically demands computational resources that grow linearly with the number of arms. Although the former problem can be remedied to some degree when prior knowledge about arm correlation is available, the latter problem persists. In this paper we propose a Thompson Sampling (TS) based scheme for exploring an arm space of size K by decomposing it into two separate arm spaces, each of size sqrtK, thus achieving sub-linear scalability. In brief, two dedicated Thompson Samplers explore each arm space separately. However, at each iteration, arm selection feedback is obtained by jointly considering the arms selected by each of the Thompson Samplers, mapping them into the original arm space. This kind of decentralized decision-making can be modeled as a game theory problem, where two independent decision makers interact in terms of a common pay-off game. Our scheme requires no communication between the decision makers, who have complete autonomy over their actions. Thus it is ideal for coordinating autonomous agents in a multi-agent system. Extensive experiments, including instances possessing multiple Nash equilibria, demonstrate remarkable performance benefits. Although TS based schemes already are among the top-performing bandit players, our proposed arm space decomposition scheme provide drastic improvements for large arm spaces, not only in terms of processing speed and memory usage, but also in terms of an improved ability to identify the optimal arm, increasing with the number of bandit arms.