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

2020

Conference Paper

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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
Department(s): Autonomous Learning, Embodied Vision
Research Project(s): Model-based Reinforcement Learning and Planning
Bibtex Type: Conference Paper (inproceedings)
State: Published
URL: https://corlconf.github.io/corl2020/paper_217/
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BibTex 

@inproceedings{PinneriEtAl2020:iCEM,
  title = {Sample-efficient Cross-Entropy Method for Real-time Planning},
  author = {Pinneri, Cristina and Sawant, Shambhuraj and Blaes, Sebastian and Achterhold, Jan and Stueckler, Joerg and Rolinek, Michal and Martius, Georg},
  booktitle = {Conference on Robot Learning 2020},
  year = {2020},
  doi = {},
  url = {https://corlconf.github.io/corl2020/paper_217/ }
}