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Model-Based Exploration in Continuous State Spaces.
Nicholas
K. Jong and Peter Stone.
In The Seventh Symposium on Abstraction,
Reformulation, and Approximation, July 2007.
[PDF]324.3kB [postscript]1.1MB
Modern reinforcement learning algorithms effectively exploit experience data sampled from an unknown controlled dynamical system to compute a good control policy, but to obtain the necessary data they typically rely on naive exploration mechansisms or human domain knowledge. Approaches that first learn a model offer improved exploration in finite problems, but discrete model representations do not extend directly to continuous problems. This paper develops a method for approximating continuous models by fitting data to a finite sample of states, leading to finite representations compatible with existing model-based exploration mechanisms. Experiments with the resulting family of fitted-model reinforcement learning algorithms reveals the critical importance of how the continuous model is generalized from finite data. This paper demonstrates instantiations of fitted-model algorithms that lead to faster learning on benchmark problems than contemporary model-free RL algorithms that only apply generalization in estimating action values. Finally, the paper concludes that in continuous problems, the exploration-exploitation tradeoff is better construed as a balance between exploration and generalization.
@InProceedings{SARA07-jong,
author="Nicholas K. Jong and Peter Stone",
title="Model-Based Exploration in Continuous State Spaces",
booktitle="The Seventh Symposium on Abstraction, Reformulation, and Approximation",
month="July",year="2007",
abstract={
Modern reinforcement learning algorithms effectively
exploit experience data sampled from an unknown
controlled dynamical system to compute a good control
policy, but to obtain the necessary data they
typically rely on naive exploration mechansisms or
human domain knowledge. Approaches that first learn
a model offer improved exploration in finite
problems, but discrete model representations do not
extend directly to continuous problems. This paper
develops a method for approximating continuous models
by fitting data to a finite sample of states, leading
to finite representations compatible with existing
model-based exploration mechanisms. Experiments with
the resulting family of fitted-model reinforcement
learning algorithms reveals the critical importance
of how the continuous model is generalized from
finite data. This paper demonstrates instantiations
of fitted-model algorithms that lead to faster
learning on benchmark problems than contemporary
model-free RL algorithms that only apply
generalization in estimating action values. Finally,
the paper concludes that in continuous problems, the
exploration-exploitation tradeoff is better construed
as a balance between exploration and generalization.
},
}
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