… We refer to algorithms which make use of a multi-step atomic backup as atomic multi-step …
atomic multi-step algorithm is characterized by its n-step return. For atomic multi-step Sarsa, …

… We also establish a connection between our multi-step … Finally, we show the advantage
of using our multi-step greedy … tal to performance, our multi-step greedy algorithms indeed …

… reinforcement learning, direct comparisons of multi-step … reinforcement learning literature.
The main reason significance has taken such a secondary role in deep reinforcement learning …

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL)
for image processing. After the introduction of the deep Q-network, deep RL has …

… This research applies a multi-agent Reinforcement Learning (RL) algorithm to SISR, creating
an advanced ensemble approach for combining powerful GANs. In our implementation …

… suggests that multi-step reinforcement learning can achieve optimal model-free predictive
control without extra training of Markov chain models [33]. Multi-step reinforcement learning is …

… The experimental results demonstrate that the proposed multi-step methods greatly improve
… The new multi-step method contains some of the best features of traditional multi-step and …

… learning speed. We also discuss the advantages and disadvantages of different ways to do
multi-step … timation and underestimation that underlies offline multi-step methods. Finally, we …

… We extended Q-learning by integrating the concept of multi-step actions (MSAs) together
with the intra-MSA method. The new MSA-Q-learning algorithm efficiently uses training …

… Similarly to the construction of Sarsa(λ,k), we could construct the multi-step variants of
other RL algorithms. For example, Q(λ,k) could be proposed based on Q-learning. …