Modified Newton Approach to Policy Search (情報論的学習理論と機械学習)

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概要

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• The natural policy gradient method was shown to be a useful approach to policy search in reinforcement learning. However, its potential weakness is that information on returns is not reflected in the metric of natural gradients, implying that it is not adaptive to data and thus less flexible. To overcome this, we propose to use Newton's method which uses the Hessian of the expected return as a metric. However, the naive implementation of Newton's method does not guarantee the Hessian to be negative definite, which causes instability on policy updates. To cope with this problem, we propose an adaptive scheme to keep the Hessian nonnegative. We demonstrate the effectiveness of our proposed method in standard reinforcement learning tasks.

• 2011-11-02

著者

• Hachiya Hirotaka

  Department Of Computer Science Tokyo Institute Of Technology

• MORIMURA Tetsuro

  IBM Research - Tokyo

• Hachiya Hirotaka

  Tokyo Inst. Of Technol.

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

• Makino Takaki

  Institute Of Industrial Science University Of Tokyo

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

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