Poster Spotlights: Automated Discovery of Options in Factored Reinforcement Learning
author:
Olga Kozlova,
ISIR - Institut des Systèmes Intelligents et de Robotique, UPMC - Université Pierre et Marie Curie - Paris 6
Description
Factored Reinforcement Learning (FRL) is a method to solve Factored Markov Decision Processes
when the structure of the transition and reward functions of the problem must be learned.
In this paper, we present TeXDYNA, an algorithm that combines the abstraction techniques
of Semi-Markov Decision Processes to perform the automatic hierarchical decomposition of the
problem with an FRL method. The algorithm is evaluated on the taxi problem.
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