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29 March 2024
 
  » arxiv » 1710.5387

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Manifold Regularization for Kernelized LSTD
Xinyan Yan ; Krzysztof Choromanski ; Byron Boots ; Vikas Sindhwani ;
Date 15 Oct 2017
AbstractPolicy 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
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