A modular hierarchical reinforcement learning algorithm

Authors:
Zhibin Liu;Xiaoqin Zeng;Huiyi Liu
Affiliations:
Institute of intelligence science and technology, Hohai University, Nanjing, China;Institute of intelligence science and technology, Hohai University, Nanjing, China;Institute of intelligence science and technology, Hohai University, Nanjing, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

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Abstract

How to improve the learning efficiency and optimize the encapsulation of subtasks is a key problem that hierarchical reinforcement learning needs to solve. This paper proposes a modular hierarchical reinforcement learning al-gorithm, named MHRL, in which the modularized hierarchical subtasks are trained by their independent reward systems. During learning, the MHRL pro-duces an optimization strategy for different modular layers, which makes inde-pendent modules be able to concurrently execute. In addition, this paper pre-sents some experimental results for solving application problems with nested learning processes. The results show that the MHRL can increase learning reus-ability and improve learning efficiency dramatically.