• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
The Open-Source TEXPLORE Code Release for Reinforcement Learning on Robots.
Todd
Hester and Peter Stone.
In Sven Behnke, Arnoud Visser, Rong Xiong, and
Manuela Veloso, editors, RoboCup-2013: Robot Soccer World Cup XVII, Lecture
Notes in Artificial Intelligence, Springer Verlag, Berlin, 2013.
[PDF]234.7kB [postscript]2.9MB
The use of robots in society could be expanded by using reinforcement learning (RL) to allow robots to learn and adapt to new situations on-line. RL is a paradigm for learning sequential decision making tasks, usually formulated as a Markov Decision Process (MDP). For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn efficiently in the face of noise, sensor/actuator delays, and continuous state features. In this paper, we present the TEXPLORE ROS code release, which contains TEXPLORE, the first algorithm to address all of these challenges together. We demonstrate TEXPLORE learning to control the velocity of an autonomous vehicle in real-time. TEXPLORE has been released as an open-source ROS repository, enabling learning on a variety of robot tasks.
@incollection{RoboCup13-hester,
author = {Todd Hester and Peter Stone},
title = {The Open-Source TEXPLORE Code Release for Reinforcement Learning on Robots},
booktitle= "RoboCup-2013: Robot Soccer World Cup {XVII}",
Editor={Sven Behnke and Arnoud Visser and Rong Xiong and Manuela Veloso},
Publisher="Springer Verlag",
address="Berlin",
year="2013",
series="Lecture Notes in Artificial Intelligence",
abstract= {
The use of robots in society could be expanded by using reinforcement
learning (RL) to allow robots to learn and adapt to new situations on-line.
RL is a paradigm for learning sequential decision making tasks, usually
formulated as a Markov Decision Process (MDP). For an RL algorithm to be
practical for robotic control tasks, it must learn in very few samples,
while continually taking actions in real-time. In addition, the algorithm
must learn efficiently in the face of noise, sensor/actuator delays, and
continuous state features. In this paper, we present the TEXPLORE ROS code
release, which contains TEXPLORE, the first algorithm to address all of
these challenges together. We demonstrate TEXPLORE learning to control the
velocity of an autonomous vehicle in real-time. TEXPLORE has been released
as an open-source ROS repository, enabling learning on a variety of robot
tasks.
},
}
Generated by bib2html.pl (written by Patrick Riley ) on Wed Apr 17, 2024 18:42:54