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Implicit incremental natural actor critic algorithm.

Ryo Iwaki1, Minoru Asada1

  • 1Osaka University, 2-1, Yamadaoka, Suita city, Osaka, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|November 9, 2018
PubMed
Summary

We introduce the implicit incremental natural actor critic (I2NAC), a stable algorithm for natural policy gradient (NPG) estimation. I2NAC demonstrates improved robustness to meta-parameter values compared to conventional methods.

Keywords:
Implicit updateIncremental learningNatural actor criticNatural policy gradientReinforcement learning

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Area of Science:

  • Reinforcement Learning
  • Machine Learning
  • Optimization Algorithms

Background:

  • Natural Policy Gradient (NPG) methods are effective for optimizing complex, high-dimensional policies.
  • Incremental NPG estimation suffers from computational instability due to sensitivity to step-size parameters.

Purpose of the Study:

  • To develop a novel, stable, and incremental algorithm for Natural Policy Gradient estimation.
  • To address the computational instability issues in conventional incremental NPG methods.

Main Methods:

  • Propose the implicit incremental natural actor critic (I2NAC) algorithm, utilizing an implicit update approach.
  • Provide convergence analysis for I2NAC.
  • Conduct numerical experiments to compare I2NAC with existing incremental NPG methods.

Main Results:

  • Theoretical analysis confirms the stability of I2NAC, contrasting with the instability of conventional incremental NPG methods.
  • Numerical experiments show I2NAC is less sensitive to meta-parameter values, including the NPG update step-size.

Conclusions:

  • I2NAC offers a more stable and robust alternative for incremental NPG estimation.
  • The implicit update mechanism enhances the reliability of policy gradient optimization in complex scenarios.