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@InProceedings{AAMAS09-hester,
author="Todd Hester and Peter Stone",
title="Generalized Model Learning for Reinforcement Learning in Factored Domains",
booktitle = "The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS)",
location = "Budapest, Hungary",
month = "May",
year = "2009",
abstract = "Improving the sample efficiency of reinforcement learning
algorithms to scale up to
larger and more realistic domains is a current research challenge
in machine learning.
Model-based methods use experiential data more efficiently than model-free
approaches but often require exhaustive exploration
to learn an accurate model of the domain.
We present an algorithm, Reinforcement Learning with
Decision Trees (\textsc{rl-dt}), that
uses supervised learning techniques to learn the model
by generalizing the relative effect of actions across states.
Specifically, \textsc{rl-dt} uses decision trees to model the
relative effects of actions in the domain.
The agent explores the environment exhaustively in early episodes
when its model is inaccurate.
Once it believes it has developed an accurate model, it exploits
its model, taking the optimal action at each step.
The combination of the learning
approach with the targeted exploration policy enables fast learning
of the model.
The sample efficiency of the algorithm is evaluated empirically
in comparison to five other algorithms across three domains.
\textsc{rl-dt} consistently accrues high cumulative rewards
in comparison with the other algorithms tested.",
wwwnote={AAMAS 2009},
}