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Reseach Article

Learning Transfer Automatic through Data Mining in Reinforcement Learning

by Zeinab Arabasadi, Nafiseh Didkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 13
Year of Publication: 2014
Authors: Zeinab Arabasadi, Nafiseh Didkar
10.5120/15411-3885

Zeinab Arabasadi, Nafiseh Didkar . Learning Transfer Automatic through Data Mining in Reinforcement Learning. International Journal of Computer Applications. 88, 13 ( February 2014), 10-12. DOI=10.5120/15411-3885

@article{ 10.5120/15411-3885,
author = { Zeinab Arabasadi, Nafiseh Didkar },
title = { Learning Transfer Automatic through Data Mining in Reinforcement Learning },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 13 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 10-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number13/15411-3885/ },
doi = { 10.5120/15411-3885 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:07:30.635081+05:30
%A Zeinab Arabasadi
%A Nafiseh Didkar
%T Learning Transfer Automatic through Data Mining in Reinforcement Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 13
%P 10-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the problems in reinforcement learning is that as the environment becomes more complex, the number of parameters used in decision making increase which leads us to a slow decision making process. The main idea here is to come up with a new algorithm which is able to transfer the information, using data mining techniques in extracting the patterns. We introduce a new algorithm for state transitions and actions which happen during the transfer by the agent are saved as a data set for data mining techniques which is presented Learning With Action Transfer (LAT). The main idea is to use the repeated action in each state, as a pattern in similar states as a means to improve learning speed and performance. The results in our algorithm will be compared to the results in Q-learning algorithm. .

References
  1. R. Sutton and A. Barto, Introduction to Reinforcement Learning, MIT Press, Cambridge (1998).
  2. L. P. Kaelbling, M. L. Littman and A. W. Moore, Reinforcement learning: A survey, Journal of Artificial Intelligence Research 4 (1996), pp. 237–285.
  3. Matthew E. Taylor, Gregory Kuhlmann, Peter Stone: Autonomous transfer for reinforcement learning. AAMAS (1) 2008: 283-290
  4. Oliver G. Selfridge, Richard S. Sutton, and Andrew G. Barto. Training and tracking in robotics.
  5. Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida, and Koh Hosoda. Vision-based behavior acquisition for a shooting robot by using a reinforcement learning. In Proceedings of IAPR/IEEE Workshop on Visual Behaviors-1994, pages 112–118, 1994.
  6. Christopher G. Atkeson and Juan C. Santamaria. A comparison of direct and model-based reinforcement learning. In Proceedings of the 1997 International Conference on Robotics and Automation.
  7. Mehran Asadi and Manfred Huber. Effective control knowledge transfer through learning skill and representation hierarchies. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2007 , pages 2054–2059.
  8. Balaraman Ravindran and Andrew G. Barto. Model minimization in hierarchical reinforcement learning. In Proceedings of the Fifth Symposium on Abstraction, Reformulation and Approximation, 2002
  9. Kimberly Ferguson and Sridhar Mahadevan. Proto-transfer learning in Markov decision processes using spectral methods. In Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, June 2006
  10. T. Dietterich, Hierarchical reinforcement learning with the MAXQ value function decomposition, Journal of Artificial Intelligence Research 3(2000) 227_303.
  11. P. Dayan, C. Watkins, Q-learning, Machine Learning 8 (1992) 279_292.
  12. Morgan Kaufmann Publishers is an imprint of Elsevier500 Sansome Street, Suite 400, San Francisco, CA94111Page468-489
  13. Cheng-Ping Lai, Pau-Choo Chung, Vincent S. Tseng: two-level clustering method for time series data analysis. Expert Syst. Appl (2010), 37
Index Terms

Computer Science
Information Sciences

Keywords

Reinforcement learning Transfer learning Data mining