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arXiv · 1502.03475

Combinatorial Bandits Revisited

Richard Combes·M. Sadegh Talebi·Alexandre Proutiere·Marc Lelarge
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Abstract

This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific regret lower bound, and discuss its scaling with the dimension of the decision space. We propose ESCB, an algorithm that efficiently exploits the structure of the problem and provide a finite-time analysis of its regret. ESCB has better performance guarantees than existing algorithms, and significantly outperforms these algorithms in practice. In the adversarial setting under bandit feedback, we propose \textsc{CombEXP}, an algorithm with the same regret scaling as state-of-the-art algorithms, but with lower computational complexity for some combinatorial problems.

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Richard Combes, M. Sadegh Talebi, Alexandre Proutiere, Marc Lelarge. 2015-02-11. Combinatorial Bandits Revisited. https://arxiv.org/abs/1502.03475

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