True Online Temporal-Difference Learning
release_m6o7pjfxyjdszizln76rrdo6ua
by
Harm van Seijen and A. Rupam Mahmood and Patrick M. Pilarski and
Marlos C. Machado and Richard S. Sutton
2016
Abstract
The temporal-difference methods TD(λ) and Sarsa(λ) form a
core part of modern reinforcement learning. Their appeal comes from their good
performance, low computational cost, and their simple interpretation, given by
their forward view. Recently, new versions of these methods were introduced,
called true online TD(λ) and true online Sarsa(λ), respectively
(van Seijen & Sutton, 2014). These new versions maintain an exact equivalence
with the forward view at all times, whereas the traditional versions only
approximate it for small step-sizes. We hypothesize that these true online
methods not only have better theoretical properties, but also dominate the
regular methods empirically. In this article, we put this hypothesis to the
test by performing an extensive empirical comparison. Specifically, we compare
the performance of true online TD(λ)/Sarsa(λ) with regular
TD(λ)/Sarsa(λ) on random MRPs, a real-world myoelectric
prosthetic arm, and a domain from the Arcade Learning Environment. We use
linear function approximation with tabular, binary, and non-binary features.
Our results suggest that the true online methods indeed dominate the regular
methods. Across all domains/representations the learning speed of the true
online methods are often better, but never worse than that of the regular
methods. An additional advantage is that no choice between traces has to be
made for the true online methods. Besides the empirical results, we provide an
in-depth analysis of the theory behind true online temporal-difference
learning. In addition, we show that new true online temporal-difference methods
can be derived by making changes to the online forward view and then rewriting
the update equations.
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