NDLI: An efficient reinforcement learning algorithm for continuous actions

Content Provider	IEEE Xplore Digital Library
Author	Fu Bo Chen Xin He Yong Wu Min
Copyright Year	2013
Description	Author affiliation: Inst. of Adv. Control & Intell. Autom., Central South Univ., Changsha, China (Fu Bo; Chen Xin; He Yong; Wu Min)
Abstract	In this paper a fast and effective reinforcement learning algorism named Dyna-CA in which the learning agent or agents can get the continuous action has been proposed to get the generalization of reinforcement learning methods to large-scale or continuous space. Firstly, the set of k states around the current state will be observed and the probability distribution of k states on condition current state can be calculated by functional mapping. Secondly the selection action-making in the current state for agent is recommended the weighted sum of best actions taken in the neighbor states to guarantee the learning with continuous actions. Then the Q value will be updated by the rules of Dyna algorism, which are not only based on the current neighbor states' practical knowledge but also the priori neighbor states' experience. Computer simulations involving the Maze and Acrobat problems illustrate the validity of the proposed reinforcement learning method and fast convergence in learning an optimal policy.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	80
Ending Page	85
File Size	259921
Page Count	6
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6560898
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Machine learning algorithms continuous actions Q-Iearning Computational modeling Learning (artificial intelligence) Dyna Probability distribution Planning Classification algorithms reinforcement learning
Content Type	Text
Resource Type	Article

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