NDLI: State thresholding to accelerate reinforcement learning

Content Provider	IEEE Xplore Digital Library
Author	Sari, S.C. Prihatmanto, A.S. Adiprawita, W. Kuspriyanto
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Bandung Inst. of Technol., Bandung, Indonesia (Prihatmanto, A.S.; Adiprawita, W.; Kuspriyanto) \|\| Dept. of Electr. Eng., Gen. Achmad Yani Univ. (UNJANI), Cimahi, Indonesia (Sari, S.C.)
Abstract	Along with the learning convergence and nonstationary equilibria, important thing to be solved in Reinforcement Learning is the slow-learning problem. In highly dynamic and stochastic systems, Markov Decision Processes is often used to model the situation, and RL is used to produce optimum control values which are expressed by optimum policy. However, approaches to solving MDP's using RL depend on storing the optimal value function or Q-value function and action models as tables do not scale to large state-spaces. The number of states grow exponentially along with the number of agents, the state and action space, which cause the learning process become very slow since it needs a very large amount of computer memory, and yet it needs more computation than the most computer performance nowadays have offered. This paper addressed curse of dimensionality problem by reducing the states in the MDP iteratively using a novel algorithm called State Thresholding in Reinforcement Learning (STRL). STRL accelerate the learning process and empirically proven to outperformed Q learning algorithm.
Starting Page	1
Ending Page	6
File Size	400792
Page Count	6
File Format	PDF
e-ISBN	9781479971893
DOI	10.1109/ICSEngT.2014.7111787
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-24
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gridworld robot navigation Navigation Computational modeling Learning (artificial intelligence) Markov processes Reinforcement Learning Acceleration Markov Decision Process Learning acceleration Robots Convergence
Content Type	Text
Resource Type	Article

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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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