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Shimon Whiteson, Matthew E. Taylor, and Peter Stone. Adaptive Tile Coding for Value Function Approximation. Technical Report AI-TR-07-339, University of Texas at Austin, 2007.
Reinforcement learning problems are commonly tackled by estimating the optimal value function. In many real-world problems, learning this value function requires a function approximator, which maps states to values via a parameterized function. In practice, the success of function approximators depends on the ability of the human designer to select an appropriate representation for the value function. This paper presents adaptive tile coding, a novel method that automates this design process for tile coding, a popular function approximator, by beginning with a simple representation with few tiles and refining it during learning by splitting existing tiles into smaller ones. In addition to automatically discovering effective representations, this approach provides a natural way to reduce the function approximator's level of generalization over time. Empirical results in multiple domains compare two different criteria for deciding which tiles to split and verify that adaptive tile coding can automatically discover effective representations and that its speed of learning is competitive with the best fixed representations.
@TechReport{whitesontr07,
author = "Shimon Whiteson and Matthew E. Taylor and Peter Stone",
title = "Adaptive Tile Coding for Value Function Approximation",
institution = "University of Texas at Austin",
number = "AI-TR-07-339",
year = 2007,
abstract={Reinforcement learning problems are commonly tackled by
estimating the optimal value function. In many real-world problems,
learning this value function requires a function approximator,
which maps states to values via a parameterized function. In
practice, the success of function approximators depends on the
ability of the human designer to select an appropriate
representation for the value function. This paper presents
\emph{adaptive tile coding}, a novel method that automates this
design process for tile coding, a popular function approximator, by
beginning with a simple representation with few tiles and refining
it during learning by splitting existing tiles into smaller
ones. In addition to automatically discovering effective
representations, this approach provides a natural way to reduce the
function approximator's level of generalization over
time. Empirical results in multiple domains compare two different
criteria for deciding which tiles to split and verify that adaptive
tile coding can automatically discover effective representations
and that its speed of learning is competitive with the best fixed
representations.},
}
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