NDLI: Approximate reinforcement learning: An overview

Content Provider	IEEE Xplore Digital Library
Author	Busoniu, L. Ernst, D. De Schutter, B. Babuska, R.
Copyright Year	2011
Description	Author affiliation: Research Associate of the FRS-FNRS; Systems and Modeling Unit, University of Liège, Belgium (Ernst, D.) \|\| Delft Center for Systems & Control, Delft Univ. of Technology, Netherlands (Busoniu, L.; De Schutter, B.; Babuska, R.)
Abstract	Reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based algorithms, RL has obtained impressive successes in robotics, artificial intelligence, control, operations research, etc. However, the scarcity of survey papers about approximate RL makes it difficult for newcomers to grasp this intricate field. With the present overview, we take a step toward alleviating this situation. We review methods for approximate RL, starting from their dynamic programming roots and organizing them into three major classes: approximate value iteration, policy iteration, and policy search. Each class is subdivided into representative categories, highlighting among others offline and online algorithms, policy gradient methods, and simulation-based techniques. We also compare the different categories of methods, and outline possible ways to enhance the reviewed algorithms.
Starting Page	1
Ending Page	8
File Size	171650
Page Count	8
File Format	PDF
ISBN	9781424498871
e-ISBN	9781424498888
DOI	10.1109/ADPRL.2011.5967353
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-11
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	function approximation Markov processes Approximation algorithms Trajectory Function approximation Mathematical model policy search Equations policy iteration reinforcement learning value iteration
Content Type	Text
Resource Type	Article

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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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