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CLC number: TP181

On-line Access: 2014-03-05

Received: 2013-09-06

Revision Accepted: 2014-01-14

Crosschecked: 2014-02-21

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.3 P.223-231


Greedy feature replacement for online value function approximation

Author(s):  Feng-fei Zhao, Zheng Qin, Zhuo Shao, Jun Fang, Bo-yan Ren

Affiliation(s):  Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; more

Corresponding email(s):   zhaofengfei@gmail.com, qingzh@tsinghua.edu.cn, shaoz09@mails.tsinghua.edu.cn

Key Words:  Reinforcement learning, Function approximation, Feature dependency, Online expansion, Feature replacement

Feng-fei Zhao, Zheng Qin, Zhuo Shao, Jun Fang, Bo-yan Ren. Greedy feature replacement for online value function approximation[J]. Journal of Zhejiang University Science C, 2014, 15(3): 223-231.

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journal="Journal of Zhejiang University Science C",
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%T Greedy feature replacement for online value function approximation
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%A Zhuo Shao
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%DOI 10.1631/jzus.C1300246

T1 - Greedy feature replacement for online value function approximation
A1 - Feng-fei Zhao
A1 - Zheng Qin
A1 - Zhuo Shao
A1 - Jun Fang
A1 - Bo-yan Ren
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 3
SP - 223
EP - 231
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Y1 - 2014
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1300246

reinforcement learning (RL) in real-world problems requires function approximations that depend on selecting the appropriate feature representations. Representational expansion techniques can make linear approximators represent value functions more effectively; however, most of these techniques function well only for low dimensional problems. In this paper, we present the greedy feature replacement (GFR), a novel online expansion technique, for value-based RL algorithms that use binary features. Given a simple initial representation, the feature representation is expanded incrementally. New feature dependencies are added automatically to the current representation and conjunctive features are used to replace current features greedily. The virtual temporal difference (TD) error is recorded for each conjunctive feature to judge whether the replacement can improve the approximation. Correctness guarantees and computational complexity analysis are provided for GFR. Experimental results in two domains show that GFR achieves much faster learning and has the capability to handle large-scale problems.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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