Automated Discovery of Options in Factored Reinforcement Learning
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چکیده
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.
منابع مشابه
Improved Automatic Discovery of Subgoals for Options in Hierarchical Reinforcement Learning
Options have been shown to be a key step in extending reinforcement learning beyond low-level reactionary systems to higher-level, planning systems. Most of the options research involves hand-crafted options; there has been only very limited work in the automated discovery of options. We extend early work in automated option discovery with a flexible and robust method.
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تاریخ انتشار 2009