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 2.7-22× less samples and yielding a performance increase of 1.2-10× in high-dimensional control problems.
Media
CoRL 2020 Presentation
Experiments Videos
Humanoid Stand-Up
Relocate
Additional Information
Links
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},
url = {https://corlconf.github.io/paper_217}
}
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},
url = {https://corlconf.github.io/paper_217}
}