On Convergence Rate of Adaptive Multiscale Value Function Approximation for Reinforcement Learning
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Quanyan Zhu | Tao Li | Quanyan Zhu | Tao Li
[1] R. DeVore,et al. Nonlinear approximation , 1998, Acta Numerica.
[2] I. Daubechies,et al. Tree Approximation and Optimal Encoding , 2001 .
[3] Andrew W. Moore,et al. Variable Resolution Discretization in Optimal Control , 2002, Machine Learning.
[4] M. Kon,et al. Convergence Rates of Multiscale and Wavelet Expansions , 2001 .
[5] Shimon Whiteson,et al. Adaptive Representations for Reinforcement Learning , 2010, Studies in Computational Intelligence.
[6] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[7] Y. Meyer. Wavelets and Operators , 1993 .
[8] Bin Han,et al. Directional compactly supported box spline tight framelets with simple geometric structure , 2019, Appl. Math. Lett..
[9] Richard S. Sutton,et al. Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[10] Richard S. Sutton,et al. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding , 1995, NIPS.
[11] Vladimir N. Temlyakov,et al. The best m-term approximation and greedy algorithms , 1998, Adv. Comput. Math..
[12] John N. Tsitsiklis,et al. Analysis of temporal-difference learning with function approximation , 1996, NIPS 1996.
[13] Richard S. Sutton,et al. Learning to predict by the methods of temporal differences , 1988, Machine Learning.
[14] Michail G. Lagoudakis,et al. Least-Squares Policy Iteration , 2003, J. Mach. Learn. Res..