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Meta-learning of Exploration/Exploitation Strategies: The Multi-armed Bandit Case

  • Francis Maes
  • Louis Wehenkel
  • Damien Ernst
Part of the Communications in Computer and Information Science book series (CCIS, volume 358)

Abstract

The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field focuses on generic solutions that can be applied to a wide range of problems. However, in practice, it is often the case that a form of prior information is available about the specific class of target problems. Prior knowledge is rarely used in current solutions due to the lack of a systematic approach to incorporate it into the E/E strategy.

To address a specific class of E/E problems, we propose to proceed in three steps: (i) model prior knowledge in the form of a probability distribution over the target class of E/E problems; (ii) choose a large hypothesis space of candidate E/E strategies; and (iii), solve an optimization problem to find a candidate E/E strategy of maximal average performance over a sample of problems drawn from the prior distribution.

We illustrate this meta-learning approach with two different hypothesis spaces: one where E/E strategies are numerically parameterized and another where E/E strategies are represented as small symbolic formulas. We propose appropriate optimization algorithms for both cases. Our experiments, with two-armed “Bernoulli” bandit problems and various playing budgets, show that the meta-learnt E/E strategies outperform generic strategies of the literature (UCB1, UCB1-Tuned, UCB-V, KL-UCB and ε n -Greedy); they also evaluate the robustness of the learnt E/E strategies, by tests carried out on arms whose rewards follow a truncated Gaussian distribution.

Keywords

Exploration-exploitation dilemma Prior knowledge Multi-armed bandit problems Reinforcement learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francis Maes
    • 1
  • Louis Wehenkel
    • 1
  • Damien Ernst
    • 1
  1. 1.Dept. of Electrical Engineering and Computer Science, Institut MontefioreUniversity of LiègeLiègeBelgium

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