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 [BibTeX] [Marc21]
Autonomous reinforcement learning with experience replay
Type of publication: Journal paper
Citation: Wawrzynski_NN_2013
Publication status: Published
Journal: Neural Networks
Volume: 41
Year: 2013
Month: May
Pages: 156 - 167
Note: Special Issue on Autonomous Learning
ISSN: 0893-6080
URL: http://www.sciencedirect.com/s...
DOI: http://dx.doi.org/10.1016/j.neunet.2012.11.007
Abstract: This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor–critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time.
Keywords: Actor–critic, Autonomous learning, reinforcement learning, Step-size estimation
Projects Idiap
Authors Wawrzyński, P.
Tanwani, Ajay Kumar
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