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Autonomous Learning Embodied Vision Conference Paper 2020

Sample-efficient Cross-Entropy Method for Real-time Planning

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Autonomous Learning
Cristina Pinneri
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Robust Machine Learning
Sebastian Blaes
  • Postdoctoral Researcher
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Embodied Vision
Jan Achterhold
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Embodied Vision
Jörg Stückler
Max Planck Research Group Leader
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Autonomous Learning
Michal Rolinek
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Empirische Inferenz, Autonomous Learning
Georg Martius
Senior Research Scientist

Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency prevents them from being used for real-time planning and control. We propose an improved version of the CEM algorithm for fast planning, with novel additions including temporally-correlated actions and memory, requiring 2.7-22x less samples and yielding a performance increase of 1.2-10x in high-dimensional control problems.

Author(s): Cristina Pinneri and Shambhuraj Sawant and Sebastian Blaes and Jan Achterhold and Joerg Stueckler and Michal Rolinek and Georg Martius
Book Title: Conference on Robot Learning 2020
Year: 2020
Project(s):
Bibtex Type: Conference Paper (inproceedings)
State: Published
URL: https://corlconf.github.io/corl2020/paper_217/
Electronic Archiving: grant_archive
Links:

BibTex 

@inproceedings{PinneriEtAl2020:iCEM,
  title = {Sample-efficient Cross-Entropy Method for Real-time Planning},
  booktitle = {Conference on Robot Learning 2020},
  abstract = {Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency prevents them from being used for real-time planning and control. We propose an improved version of the CEM algorithm for fast planning, with novel additions including temporally-correlated actions and memory, requiring 2.7-22x less samples and yielding a performance increase of 1.2-10x in high-dimensional control problems.},
  year = {2020},
  slug = {pinnerietal2020-icem},
  author = {Pinneri, Cristina and Sawant, Shambhuraj and Blaes, Sebastian and Achterhold, Jan and Stueckler, Joerg and Rolinek, Michal and Martius, Georg},
  url = {https://corlconf.github.io/corl2020/paper_217/ }
}