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Feature Selection for Value Function Approximation Using Bayesian Model Selection.
Tobias
Jung and Peter Stone.
In The European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Databases, September 2009.
[PDF]746.9kB [postscript]2.3MB [slides.pdf]957.5kB
Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unknown value function can be obtained, is one of the main challenges in scaling RL to real-world applications. Here we consider the Gaussian process based framework GPTD for approximate policy evaluation, and propose feature selection through marginal likelihood optimization of the associated hyperparameters. Our approach has two appealing benefits: (1) given just sample transitions, we can solve the policy evaluation problem fully automatically (without looking at the learning task, and, in theory, independent of the dimensionality of the state space), and (2) model selection allows us to consider more sophisticated kernels, which in turn enable us to identify relevant subspaces and eliminate irrelevant state variables such that we can achieve substantial computational savings and improved prediction performance.
@InProceedings{ECML09-jung,
author="Tobias Jung and Peter Stone",
title="Feature Selection for Value Function Approximation Using Bayesian Model Selection",
booktitle="The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases",
month="September",
year="2009",
abstract={Feature selection in reinforcement learning (RL), i.e. choosing
basis functions such that useful approximations of the unknown value
function can be obtained, is one of the main challenges in scaling RL to
real-world applications. Here we consider the Gaussian process based
framework GPTD for approximate policy evaluation, and propose feature
selection through marginal likelihood optimization of the associated
hyperparameters. Our approach has two appealing benefits: (1) given just
sample transitions, we can solve the policy evaluation problem fully
automatically (without looking at the learning task, and, in theory,
independent of the dimensionality of the state space), and (2) model
selection allows us to consider more sophisticated kernels, which in turn
enable us to identify relevant subspaces and eliminate irrelevant state
variables such that we can achieve substantial computational savings and
improved prediction performance.
},
}
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