Using Strongly Connected Components as a Basis for Autonomous Skill Acquisition in Reinforcement Learning

Authors:
Seyed Jalal Kazemitabar;Hamid Beigy
Affiliations:
Intelligent Systems Lab. Computer Eng. Department, Sharif Univeristy of Technology,;Intelligent Systems Lab. Computer Eng. Department, Sharif Univeristy of Technology,
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

Hierarchical reinforcement learning (HRL) has had a vast range of applications in recent years. Preparing mechanisms for autonomous acquisition of skills has been a main topic of research in this area. While different methods have been proposed to achieve this goal, few methods have been shown to be successful both in performance and also efficiency in terms of time complexity of the algorithm. In this paper, a linear time algorithm is proposed to find subgoal states of the environment in early episodes of learning. Having subgoals available in early phases of a learning task, results in building skills that dramatically increase the convergence rate of the learning process.