Poster Spotlights: Automated Discovery of Options in Factored Reinforcement Learning

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.