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Model-based Reinforcement Learning in a Complex Domain.
Shivaram
Kalyanakrishnan, Peter Stone, and Yaxin
Liu.
In Ubbo Visser, Fernando Ribeiro, Takeshi Ohashi, and Frank Dellaert, editors, RoboCup-2007: Robot Soccer World
Cup XI, Lecture Notes in Artificial Intelligence, pp. 171–83, Springer Verlag, Berlin, 2008.
[PDF]200.9kB [postscript]515.0kB
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the environment. Model-free algorithms perform updates solely based on observed experiences. By contrast, model-based algorithms learn a model of the environment that effectively simulates its dynamics. The model may be used to simulate experiences or to plan into the future, potentially expediting the learning process. This paper presents a model-based reinforcement learning approach for Keepaway, a complex, continuous, stochastic, multiagent subtask of RoboCup simulated soccer. First, we propose the design of an environmental model that is partly learned based on the agent's experiences. This model is then coupled with the reinforcement learning algorithm to learn an action selection policy. We evaluate our method through empirical comparisons with model-free approaches that have been previously applied successfully to this task. Results demonstrate significant gains in the learning speed and asymptotic performance of our method. We also show that the learned model can be used effectively as part of a planning-based approach with a hand-coded policy.
@incollection(LNAI2007-shivaram,
author="Shivaram Kalyanakrishnan and Peter Stone and Yaxin Liu",
title="Model-based Reinforcement Learning in a Complex Domain",
booktitle= "{R}obo{C}up-2007: Robot Soccer World Cup {XI}",
Editor="Ubbo Visser and Fernando Ribeiro and Takeshi Ohashi and Frank Dellaert",
Publisher="Springer Verlag",address="Berlin",year="2008",
series="Lecture Notes in Artificial Intelligence",
volume="5001",
pages="171--83",
abstract={Reinforcement learning is a paradigm under which an
agent seeks to improve its policy by making learning
updates based on the experiences it gathers through
interaction with the environment. \emph{Model-free}
algorithms perform updates solely based on observed
experiences. By contrast, \emph{model-based}
algorithms learn a model of the environment that
effectively simulates its dynamics. The model may be
used to simulate experiences or to plan into the
future, potentially expediting the learning
process. This paper presents a model-based
reinforcement learning approach for Keepaway, a
complex, continuous, stochastic, multiagent subtask
of RoboCup simulated soccer. First, we propose the
design of an environmental model that is partly
learned based on the agent's experiences. This
model is then coupled with the reinforcement
learning algorithm to learn an action selection
policy. We evaluate our method through empirical
comparisons with model-free approaches that have
been previously applied successfully to this
task. Results demonstrate significant gains in the
learning speed and asymptotic performance of our
method. We also show that the learned model can be
used effectively as part of a planning-based
approach with a hand-coded policy.},
wwnote = {Official version from <a href="http://dx.doi.org/10.1007/978-3-540-68847-1_15">Publisher's Webpage</a>© Springer-Verlag},
)
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