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arXiv · 2011.00213

Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs

Wenhao Yang·Xiang Li·Guangzeng Xie·Zhihua Zhang
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Abstract

Regularized MDPs serve as a smooth version of original MDPs. However, biased optimal policy always exists for regularized MDPs. Instead of making the coefficient{\lambda}of regularized term sufficiently small, we propose an adaptive reduction scheme for {\lambda} to approximate optimal policy of the original MDP. It is shown that the iteration complexity for obtaining an{\epsilon}-optimal policy could be reduced in comparison with setting sufficiently small{\lambda}. In addition, there exists strong duality connection between the reduction method and solving the original MDP directly, from which we can derive more adaptive reduction method for certain algorithms.

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Wenhao Yang, Xiang Li, Guangzeng Xie, Zhihua Zhang. 2020-10-31. Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs. https://arxiv.org/abs/2011.00213

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