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On Entropy Regularized Path Integral Control for Trajectory Optimization.

Tom Lefebvre1,2, Guillaume Crevecoeur1,2

  • 1Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9000 Ghent, Belgium.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

Path Integral Control (PIC) methods are generalized for policy search, extending to Entropy Regularized Trajectory Optimization. This approach connects stochastic optimal control with reinforcement learning for derivative-free trajectory optimization.

Keywords:
entropic inferenceentropy regularizationpath integral controlstochastic search methods

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Area of Science:

  • Control Theory
  • Machine Learning
  • Optimization

Background:

  • Path Integral Control (PIC) is a policy search method linked to Linearly Solvable Optimal Control (LSOC).
  • LSOC is a subclass of nonlinear Stochastic Optimal Control (SOC) problems solvable explicitly for optimal state trajectory distributions.

Purpose of the Study:

  • To generalize Path Integral Control (PIC) methods beyond the LSOC setting.
  • To formulate a new SOC problem, Entropy Regularized Trajectory Optimization, related to LSOC.
  • To analyze the convergence and connect with Reinforcement Learning (RL) for trajectory optimization.

Main Methods:

  • Reviewing PIC theory and related policy search algorithms.
  • Identifying a generic design strategy based on optimal state trajectory distributions.
  • Formulating and analyzing Entropy Regularized Trajectory Optimization.
  • Deriving explicit updates for Entropy Regularized PIC.

Main Results:

  • A generalized view of PIC methods is presented.
  • Entropy Regularized Trajectory Optimization is formulated, sharing traits with LSOC.
  • Theoretical convergence of trajectory distributions is analyzed.
  • Connections are drawn between PIC, RL, and classic optimization.

Conclusions:

  • The generalized PIC framework enables broader applications in policy search.
  • Entropy Regularized Trajectory Optimization offers a less restrictive approach to SOC problems.
  • The derived methods provide explicit updates for derivative-free trajectory optimization, linking PIC and RL.