  The true online TD({\lambda}) algorithm has recently been proposed (van
Seijen and Sutton, 2014) as a universal replacement for the popular
TD({\lambda}) algorithm, in temporal-difference learning and reinforcement
learning. True online TD({\lambda}) has better theoretical properties than
conventional TD({\lambda}), and the expectation is that it also results in
faster learning. In this paper, we put this hypothesis to the test.
Specifically, we compare the performance of true online TD({\lambda}) with that
of TD({\lambda}) on challenging examples, random Markov reward processes, and a
real-world myoelectric prosthetic arm. We use linear function approximation
with tabular, binary, and non-binary features. We assess the algorithms along
three dimensions: computational cost, learning speed, and ease of use. Our
results confirm the strength of true online TD({\lambda}): 1) for sparse
feature vectors, the computational overhead with respect to TD({\lambda}) is
minimal; for non-sparse features the computation time is at most twice that of
TD({\lambda}), 2) across all domains/representations the learning speed of true
online TD({\lambda}) is often better, but never worse than that of
TD({\lambda}), and 3) true online TD({\lambda}) is easier to use, because it
does not require choosing between trace types, and it is generally more stable
with respect to the step-size. Overall, our results suggest that true online
TD({\lambda}) should be the first choice when looking for an efficient,
general-purpose TD method.
