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Article overview
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Manifold Regularization for Kernelized LSTD | Xinyan Yan
; Krzysztof Choromanski
; Byron Boots
; Vikas Sindhwani
; | Date: |
15 Oct 2017 | Abstract: | Policy evaluation or value function or Q-function approximation is a key
procedure in reinforcement learning (RL). It is a necessary component of policy
iteration and can be used for variance reduction in policy gradient methods.
Therefore its quality has a significant impact on most RL algorithms. Motivated
by manifold regularized learning, we propose a novel kernelized policy
evaluation method that takes advantage of the intrinsic geometry of the state
space learned from data, in order to achieve better sample efficiency and
higher accuracy in Q-function approximation. Applying the proposed method in
the Least-Squares Policy Iteration (LSPI) framework, we observe superior
performance compared to widely used parametric basis functions on two standard
benchmarks in terms of policy quality. | Source: | arXiv, 1710.5387 | Services: | Forum | Review | PDF | Favorites |
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