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State representation learning for control: An overview.

Timothée Lesort1, Natalia Díaz-Rodríguez2, Jean-Frano Is Goudou3

  • 1Vision Lab, Thales, Theresis, Palaiseau, France; U2IS, ENSTA ParisTech, Inria FLOWERS team, Universite Paris Saclay, Palaiseau, France.

Neural Networks : the Official Journal of the International Neural Network Society
|September 30, 2018
PubMed
Summary
This summary is machine-generated.

State representation learning (SRL) creates low-dimensional features for dynamic environments, aiding robotics and control. This survey reviews recent SRL methods, their applications, and evaluation techniques.

Keywords:
Disentanglement of control factorsLearning disentangled representationsLow dimensional embedding learningReinforcement learningRoboticsState representation learning

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Machine Learning

Background:

  • Representation learning aims to extract meaningful features from data.
  • State representation learning (SRL) focuses on low-dimensional, time-evolving features influenced by agent actions.
  • SRL is crucial for robotics and control, addressing the curse of dimensionality and enhancing policy learning.

Purpose of the Study:

  • To survey the state-of-the-art in state representation learning.
  • To review recent SRL methods, implementations, and applications in robotics.
  • To highlight diverse learning objectives and evaluation strategies in SRL.

Main Methods:

  • Review of recent literature on state representation learning.
  • Analysis of SRL methods involving environment interaction.
  • Categorization of methods based on learning objectives and applications.

Main Results:

  • Identified various SRL techniques and their implementations.
  • Demonstrated the utility of SRL in simulated and real-world robotics control tasks.
  • Highlighted how different learning objectives are applied in SRL algorithms.

Conclusions:

  • SRL is a vital component for advanced robotics and control systems.
  • Further research is needed in evaluation methods and future directions for SRL.
  • SRL offers significant benefits, including improved performance and interpretability.