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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.}
}