… Their book is an authoritative but reader-friendly introduction to many of the central ideas of
RL, and offers a wealth of well-chosen examples. At the same time, Sutton and Barto explain …

… work focused on supervised learning. Here, we focus on making an “interpretable-by-design”
deep reinforcement learning agent which is forced to use human-friendly prototypes in its …

… the meaning of AI and machine learning (ML) by focusing more on reinforcement learning
(RL). … and the approach through model learning, direct reinforcement learning, and planning. …

… reinforcement learning. One particular direction of abstraction in reinforcement learning is
hierarchical reinforcement learning… Early work includes learning with options [Sutton et al., …

… Addressing these obstacles, we present EasyRL4Rec, a user-friendly and efficient library
tailored for RL-based RSs. … 1RL in this work specifically refers to deep reinforcement learning. …

… Jasmin, EA; Imthias Ahamed, TP; Jagathy Raj, VP Reinforcement learning solution for unit
… A function approximation approach to reinforcement learning for solving unit commitment …

… with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL…
agents and autonomous systems, called deep reinforcement learning (DRL). In this paper, …

… implementation for reinforcement learning based on … commonly used benchmark for
reinforcement learning algorithms [12]–[… • We introduce a two-fold reinforcement learning …

… Specifically, we present ARtonomous, a youth-friendly, tablet-based tool for learning about
and generating reinforcement learning models for robot navigation. This system supports use …

… reinforcement learning research is conducted. We help you to get started with machine
learning … there is a straight line to another friendly disc, the opponent’s discs change color to the …