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Off-Policy Reinforcement Learning with Gaussian Processes

IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 227-238页

作者： Girish Chowdhary Miao Liu Robert Grande Thomas Walsh Jonathan How Lawrence Carin Oklahomas State University Duke University Massachusetts Institute of Technology

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 *** 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 *** results demonstrate GPQ has competitive learning speed in addition to its convergence guarantees and its ability to automatically choose its own bases locations.

关键词： Approximation algorithms Convergence Function approximation Gaussian processes Learning (artificial intelligence) bayesian nonparametric Reinforcement learning off-policy learning Converge learning (artificial intelligence) Approximation algorithms Gaussian processes function approximation Learning

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ROBUST RVM BASED ON SPIKE-SLAB PRIOR

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Journal of Electronics(China) 2012年第6期29卷 593-597页

作者： Ding Xinghao Mi Zengyuan Huang Yue Jin Wenbo School of Information Science and Technology Xiamen University

Although Relevance Vector Machine (RVM) is the most popular algorithms in machine learning and computer vision, outliers in the training data make the estimation unreliable. In the paper, a robust RVM model under non-parametric bayesian framework is proposed. We decompose the noise term in the RVM model into two components, a Gaussian noise term and a spiky noise term. Therefore the observed data is assumed represented as: where is the relevance vector component, of which is the kernel function matrix and is the weight matrix, is the spiky term and is the Gaussian noise term. A spike-slab sparse prior is imposed on the weight vector which gives a more intuitive constraint on the sparsity than the Student's t-distribution described in the traditional RVM. For the spiky component a spike-slab sparse prior is also introduced to recognize outliers in the training data effectively. Several experiments demonstrate the better performance over the RVM regression.

关键词： Relevance Vector Machine (RVM) bayesian nonparametric Outliers Spike-slab sparse prior

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