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Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning.
Shimon Whiteson, Matthew
E. Taylor, and Peter Stone.
Journal of Autonomous Agents and
Multi-Agent Systems, 21(1):1–27, 2010.
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Temporal difference and evolutionary methods are two of the most common approaches to solving reinforcement learning problems. However, there is little consensus on their relative merits and there have been few empirical studies that directly compare their performance. This article aims to address this shortcoming by presenting results of empirical comparisons between Sarsa and NEAT, two representative methods, in mountain car and keepaway, two benchmark reinforcement learning tasks. In each task, the methods are evaluated in combination with both linear and nonlinear representations to determine their best configurations. In addition, this article tests two specific hypotheses about the critical factors contributing to these methods' relative performance: 1) that sensor noise reduces the final performance of Sarsa more than that of NEAT, because Sarsa's learning updates are not reliable in the absence of the Markov property and 2) that stochasticity, by introducing noise in fitness estimates, reduces the learning speed of NEAT more than that of Sarsa. Experiments in variations of mountain car and keepaway designed to isolate these factors confirm both these hypotheses.
@Article{JAAMAS09-Whiteson,
Author="Shimon Whiteson and Matthew E.\ Taylor and Peter Stone",
title="Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning",
journal="Journal of Autonomous Agents and Multi-Agent Systems",
volume="21",number="1",pages="1--27",
year="2010",
abstract="Temporal difference and evolutionary methods are two
of the most common approaches to solving reinforcement
learning problems. However, there is little consensus on their
relative merits and there have been few empirical studies that
directly compare their performance. This article aims to
address this shortcoming by presenting results of empirical
comparisons between Sarsa and NEAT, two representative
methods, in mountain car and keepaway, two benchmark
reinforcement learning tasks. In each task, the methods are
evaluated in combination with both linear and nonlinear
representations to determine their best configurations. In
addition, this article tests two specific hypotheses about the
critical factors contributing to these methods' relative
performance: 1) that sensor noise reduces the final
performance of Sarsa more than that of NEAT, because Sarsa's
learning updates are not reliable in the absence of the Markov
property and 2) that stochasticity, by introducing noise in
fitness estimates, reduces the learning speed of NEAT more
than that of Sarsa. Experiments in variations of mountain car
and keepaway designed to isolate these factors confirm both
these hypotheses.",
}
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