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}) ha
s 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.
