… supply chain drivers, algorithms, data sources, … learning algorithm is still the most popular
one. Second, inventory management is the most common application of reinforcement learning …

… This chapter contains a survey of these concepts, along with a survey of the new class of Zap
reinforcement learning algorithms introduced by the authors. These algorithms can achieve …

… using the same algorithm and network … reinforcement learning algorithm can learn, tabula
rasa—without domain-specific human knowledge or data, as evidenced by the same algorithm …

… Abstract--Most of the research in reinforcement learning has … a stochastic reinforcement
learning algorithm.~br learning[… Learning takes place bv using our algorithm to arlfl,st the two …

… the algorithms nor the analyses continue to be usable. We propose and analyze a new
learning algorithm to solve a certain class of non-Markov decision problems. Our algorithm …

… adjusting the time steps in these discrete-time algorithms to the scale of the task. But is this
… that the algorithm could not accomplish by itself? If reinforcement learning algorithms cannot …

… is quite different from supervised learning where an input is … that uses reinforcement learning
algorithms to solve … leads to competitive supervised learning algorithms, and what possible …

… In this section we discuss some notable prior work on reinforcement learning in computer
chess. NeuroChess (31) evaluated positions by a neural network that used 175 handcrafted …

… We derive a practical gradient-based meta-learning algorithm and show that this can
significantly improve performance on large-scale deep reinforcement learning applications. …

… Trust us; even if you are familiar with reinforcement learning, you … to Part 2 to learn
about reinforcement learning algorithms. … to start its algorithmic part with a description of Q-learning, …