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An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning
In this paper we introduce the idea of improving the performance of
parametric temporal-difference (TD) learning algorithms by selectively
emphasizing or de-emphasizing their updates on different time steps. In
particular, we show that varying the emphasis of linear TD()'s updates
in a particular way causes its expected update to become stable under
off-policy training. The only prior model-free TD methods to achieve this with
per-step computation linear in the number of function approximation parameters
are the gradient-TD family of methods including TDC, GTD(), and
GQ(). Compared to these methods, our _emphatic TD()_ is
simpler and easier to use; it has only one learned parameter vector and one
step-size parameter. Our treatment includes general state-dependent discounting
and bootstrapping functions, and a way of specifying varying degrees of
interest in accurately valuing different states.Comment: 29 pages This is a significant revision based on the first set of
reviews. The most important change was to signal early that the main result
is about stability, not convergenc
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