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.
Efficient planning plays a crucial role in model-based reinforcement learning. Traditionally, the main planning operation is a full backup based on the current estimates of the successor states. Consequently, its computation time is proportional to t
he number of successor states. In this paper, we introduce a new planning backup that uses only the current value of a single successor state and has a computation time independent of the number of successor states. This new backup, which we call a small backup, opens the door to a new class of model-based reinforcement learning methods that exhibit much finer control over their planning process than traditional methods. We empirically demonstrate that this increased flexibility allows for more efficient planning by showing that an implementation of prioritized sweeping based on small backups achieves a substantial performance improvement over classical implementations.
