%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Wawrzynski_NN_2013,
                      author = {Wawrzy{\'{n}}ski, P. and Tanwani, Ajay Kumar},
                    keywords = {Actor–critic, Autonomous learning, reinforcement learning, Step-size estimation},
                    projects = {Idiap},
                       month = may,
                       title = {Autonomous reinforcement learning with experience replay},
                     journal = {Neural Networks},
                      volume = {41},
                      number = {0},
                        year = {2013},
                       pages = {156 - 167},
                        note = {Special Issue on Autonomous Learning},
                        issn = {0893-6080},
                         url = {http://www.sciencedirect.com/science/article/pii/S0893608012002936},
                         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.}
}