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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Policy Search in a Space of Simple Closed-form Formulas: Towards Interpretability of Reinforcement Learning
verfasst von : Francis Maes, Raphael Fonteneau, Louis Wehenkel, Damien Ernst
Erschienen in: Discovery Science
Verlag: Springer Berlin Heidelberg
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In this paper, we address the problem of computing interpretable solutions to reinforcement learning (RL) problems. To this end, we propose a search algorithm over a space of simple closed-form formulas that are used to rank actions. We formalize the search for a high-performance policy as a multi-armed bandit problem where each arm corresponds to a candidate policy canonically represented by its shortest formula-based representation. Experiments, conducted on standard benchmarks, show that this approach manages to determine both efficient and interpretable solutions.