Poster
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton · Csaba Szepesvari · Sharan Vaswani · Zheng Wen · Tor Lattimore · Mohammad Ghavamzadeh

Wed Jun 12th 06:30 -- 09:00 PM @ Pacific Ballroom #125
in Posters Wed »

We propose a bandit algorithm that explores by randomizing its history of rewards. Specifically, it pulls the arm with the highest mean reward in a non-parametric bootstrap sample of its history with pseudo rewards. We design the pseudo rewards such that the bootstrap mean is optimistic with a sufficiently high probability. We call our algorithm Giro, which stands for garbage in, reward out. We analyze Giro in a Bernoulli bandit and derive a $O(K \Delta^{-1} \log n)$ bound on its $n$-round regret, where $\Delta$ is the difference in the expected rewards of the optimal and the best suboptimal arms, and $K$ is the number of arms. The main advantage of our exploration design is that it easily generalizes to structured problems. To show this, we propose contextual Giro with an arbitrary reward generalization model. We evaluate Giro and its contextual variant on multiple synthetic and real-world problems, and observe that it performs well.

Keywords: Bandits · Online Learning

Author Information

Branislav Kveton (Google Research)
Csaba Szepesvari (DeepMind/University of Alberta)
Sharan Vaswani (Mila, University of Montreal)
Zheng Wen (Adobe Research)
Tor Lattimore (DeepMind)
Mohammad Ghavamzadeh (Facebook AI Research)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors