A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 3
Jul.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Girish Chowdhary, Miao Liu, Robert Grande, Thomas Walsh, Jonathan How and Lawrence Carin, "Off-Policy Reinforcement Learning with Gaussian Processes," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 227-238, 2014.
Citation: Girish Chowdhary, Miao Liu, Robert Grande, Thomas Walsh, Jonathan How and Lawrence Carin, "Off-Policy Reinforcement Learning with Gaussian Processes," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 227-238, 2014.

Off-Policy Reinforcement Learning with Gaussian Processes

Funds:

This work was supported by Office of Naval Research Science of Autonomy Program (N000140910625).

  • Abstract—An off-policy Bayesian nonparameteric approximate reinforcement learning framework, termed as GPQ, that employs a Gaussian processes (GP) model of the value (Q) function is presented in both the batch and online settings. Sufficient conditions on GP hyperparameter selection are established to guarantee convergence of off-policy GPQ in the batch setting, and theoretical and practical extensions are provided for the online case. Empirical results demonstrate GPQ has competitive learning speed in addition to its convergence guarantees and its ability to automatically choose its own bases locations.

     

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