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Model-based reinforcement learning with dimension reduction.

Voot Tangkaratt1, Jun Morimoto2, Masashi Sugiyama3

  • 1Department of Computer Science, The University of Tokyo, Japan.

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|September 19, 2016
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Summary
This summary is machine-generated.

This study introduces a novel approach combining model-based reinforcement learning with least-squares conditional entropy (LSCE) for efficient high-dimensional control. The method effectively estimates environment models and reduces dimensions, outperforming existing techniques in complex tasks.

Keywords:
Model-based reinforcement learningSufficient dimension reductionTransition model estimation

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Reinforcement learning aims to optimize agent policies for maximum cumulative reward.
  • Model-based reinforcement learning relies on learning environment transition models, which is data-intensive in high-dimensional settings.

Purpose of the Study:

  • To address the challenge of data inefficiency in high-dimensional model-based reinforcement learning.
  • To propose a novel method combining model-based reinforcement learning with least-squares conditional entropy (LSCE).

Main Methods:

  • Integrating LSCE for simultaneous transition model estimation and dimension reduction.
  • Extending the LSCE-enhanced approach to imitation learning scenarios.

Main Results:

  • The proposed LSCE-based method demonstrates effective policy search in high-dimensional control tasks.
  • Successful application shown in real humanoid robot control experiments.

Conclusions:

  • Combining model-based reinforcement learning with LSCE offers a data-efficient solution for high-dimensional control.
  • The method shows promise for complex robotic applications and imitation learning.