Representation Discovery for Kernel-Based Reinforcement Learning
Recent years have seen increased interest in non-parametric reinforcement learning. There are now practical kernel-based algorithms for approximating value functions; however, kernel regression requires that the underlying function being approximated be smooth on its domain. Few problems of interest...
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2015
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| Online Access: | http://hdl.handle.net/1721.1/100053 |