NDLI: Feature selection and feature learning for high-dimensional batch reinforcement learning: A survey

Content Provider	Springer Nature Link
Author	Liu, De Rong Li, Hong Liang Wang, Ding
Copyright Year	2015
Abstract	Tremendous amount of data are being generated and saved in many complex engineering and social systems every day. It is significant and feasible to utilize the big data to make better decisions by machine learning techniques. In this paper, we focus on batch reinforcement learning (RL) algorithms for discounted Markov decision processes (MDPs) with large discrete or continuous state spaces, aiming to learn the best possible policy given a fixed amount of training data. The batch RL algorithms with handcrafted feature representations work well for low-dimensional MDPs. However, for many real-world RL tasks which often involve high-dimensional state spaces, it is difficult and even infeasible to use feature engineering methods to design features for value function approximation. To cope with high-dimensional RL problems, the desire to obtain data-driven features has led to a lot of works in incorporating feature selection and feature learning into traditional batch RL algorithms. In this paper, we provide a comprehensive survey on automatic feature selection and unsupervised feature learning for high-dimensional batch RL. Moreover, we present recent theoretical developments on applying statistical learning to establish finite-sample error bounds for batch RL algorithms based on weighted L $_{ p }$ norms. Finally, we derive some future directions in the research of RL algorithms, theories and applications.
Starting Page	229
Ending Page	242
Page Count	14
File Format	PDF
ISSN	14768186
Journal	International Journal of Automation and Computing
Volume Number	12
Issue Number	3
e-ISSN	17518520
Language	English
Publisher	Institute of Automation, Chinese Academy of Sciences
Publisher Date	2015-05-09
Publisher Place	Beijing
Access Restriction	Subscribed
Subject Keyword	Intelligent control reinforcement learning adaptive dynamic programming feature selection feature learning big data Control, Robotics, Mechatronics Computer Applications Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Systems Engineering Modeling and Simulation Computer Science Applications

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