Sample-Efficient Cross-Entropy Method for Real-Time Planning
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.
Cite
Text
Pinneri et al. "Sample-Efficient Cross-Entropy Method for Real-Time Planning." Conference on Robot Learning, 2020.Markdown
[Pinneri et al. "Sample-Efficient Cross-Entropy Method for Real-Time Planning." Conference on Robot Learning, 2020.](https://mlanthology.org/corl/2020/pinneri2020corl-sampleefficient/)BibTeX
@inproceedings{pinneri2020corl-sampleefficient,
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},
year = {2020},
pages = {1049-1065},
volume = {155},
url = {https://mlanthology.org/corl/2020/pinneri2020corl-sampleefficient/}
}