NDLI: Generalized model learning for Reinforcement Learning on a humanoid robot

Content Provider	IEEE Xplore Digital Library
Author	Hester, T. Quinlan, M. Stone, P.
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, The University of Texas at Austin, 78712, USA (Hester, T.; Quinlan, M.; Stone, P.)
Abstract	Reinforcement learning (RL) algorithms have long been promising methods for enabling an autonomous robot to improve its behavior on sequential decision-making tasks. The obvious enticement is that the robot should be able to improve its own behavior without the need for detailed step-by-step programming. However, for RL to reach its full potential, the algorithms must be sample efficient: they must learn competent behavior from very few real-world trials. From this perspective, model-based methods, which use experiential data more efficiently than model-free approaches, are appealing. But they often require exhaustive exploration to learn an accurate model of the domain. In this paper, we present an algorithm, Reinforcement Learning with Decision Trees (RL-DT), that uses decision trees to learn the model by generalizing the relative effect of actions across states. The agent explores the environment until it believes it has a reasonable policy. The combination of the learning approach with the targeted exploration policy enables fast learning of the model. We compare RL-DT against standard model-free and model-based learning methods, and demonstrate its effectiveness on an Aldebaran Nao humanoid robot scoring goals in a penalty kick scenario.
Starting Page	2369
Ending Page	2374
File Size	1685457
Page Count	6
File Format	PDF
ISBN	9781424450381
ISSN	10504729
DOI	10.1109/ROBOT.2010.5509181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humanoid robots Decision trees Machine learning Learning systems Robotics and automation USA Councils Computer science Decision making Robot programming Helicopters
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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