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On Optimizing Interdependent Skills: A Case Study in Simulated 3D Humanoid Robot Soccer.
Daniel
Urieli, Patrick MacAlpine, Shivaram
Kalyanakrishnan, Yinon Bentor, and Peter
Stone.
In Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), pp. 769–776, IFAAMAS,
May 2011.
Accompanying videos at http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2010/html/skilloptimization2010.html
[PDF]530.9kB [postscript]1.6MB [slides.pptx]3.8MB
In several realistic domains an agent's behavior is composed of multiple interdependent skills. For example, consider a humanoid robot that must play soccer, as is the focus of this paper. In order to succeed, it is clear that the robot needs to walk quickly, turn sharply, and kick the ball far. However, these individual skills are ineffective if the robot falls down when switching from walking to turning, or if it cannot position itself behind the ball for a kick. This paper presents a learning architecture for a humanoid robot soccer agent that has been fully deployed and tested within the RoboCup 3D simulation environment. First, we demonstrate that individual skills such as walking and turning can be parameterized and optimized to match the best performance statistics reported in the literature. These results are achieved through effective use of the CMA-ES optimization algorithm. Next, we describe a framework for optimizing skills in conjunction with one another, a little-understood problem with substantial practical significance. Over several phases of learning, a total of roughly 100--150 parameters are optimized. Detailed experiments show that an agent thus optimized performs comparably with the top teams from the RoboCup 2010 competitions, while taking relatively few man-hours for development.
@InProceedings{AAMAS11-urieli,
author="Daniel Urieli and Patrick MacAlpine and Shivaram Kalyanakrishnan and Yinon Bentor and Peter Stone",
title="On Optimizing Interdependent Skills: A Case Study in Simulated 3D Humanoid Robot Soccer",
booktitle="Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS)",
month="May",
year="2011",
abstract={ In several realistic domains an agent's behavior
is composed of multiple \textit{interdependent} skills. For example,
consider a humanoid robot that must play soccer, as is the focus of this
paper. In order to succeed, it is clear that the robot needs to walk
quickly, turn sharply, and kick the ball far. However, these
individual skills are ineffective if the robot falls down when
switching from walking to turning, or if it cannot position itself
behind the ball for a kick.
This paper presents a learning architecture for a humanoid robot
soccer agent that has been fully deployed and tested within the
RoboCup 3D simulation environment. First, we demonstrate that
individual skills such as walking and turning can be parameterized and
optimized to match the best performance statistics reported in the
literature. These results are achieved through effective use of the
CMA-ES optimization algorithm. Next, we describe a framework for
optimizing skills \textit{in conjunction} with one another, a
little-understood problem with substantial practical
significance. Over several phases of learning, a total of roughly 100--150
parameters are optimized. Detailed experiments show that an agent thus
optimized performs comparably with the top teams from the RoboCup 2010
competitions, while taking relatively few man-hours for development. },
wwwnote={Accompanying videos at <a href="http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2010/html/skilloptimization2010.html">http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2010/html/skilloptimization2010.html</a>},
ISBN = "978-0-9826571-5-7",
editor = "Tumer, Kagan and Yolum, Pinar and Sonenberg, Liz and Stone, Peter",
volume = "2",
publisher = "IFAAMAS",
pages = "769--776",
}
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