Autonomous Learning
Embodied Vision
Conference Paper
2020
Sample-efficient Cross-Entropy Method for Real-time Planning
Autonomous Learning
Embodied Vision
Autonomous Learning
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/ }
}