… fundamentals of classical reinforcement learning and provide a general overview of deep
reinforcement learning… definitions and concepts of reinforcement learning, including the agent, …

… entire system that involves Reinforcement Learning. Concepts such … algorithms in
Reinforcement Learning, the Q-Learning algorithm. … To do this, we will understand the …

… Inspired by the success of machine learning in solving complicated control and decision-…
deep reinforcement-learning (DRL)–based approaches that allow network entities to learn and …

… learning into reinforcement learning, DRL is not only capable of continuing sensing and
learning … In this section, we briefly review related work of deep reinforcement learning in main IR …

… This manuscript covers all important fundamental aspects of reinforcement learning. In
chapter2, Markov Decision Process is presented to model the interaction between the agent and …

… Reinforcement Learning (RL) is another type of learning in AI that focuses on training by
interacting with the environment. In contrast to supervised learning, where an agent learns from …

… is to provide an introduction to reinforcement learning (RL) at a … The fundamental principles
and techniques used to solve RL … In Section 2 the parts of a reinforcement learning problem …

… This book provides the most reliable entry to deep reinforcement learning, bridging the gap
between fundamentals and practices, featuring detailed explanation and demonstration of …

… This book is on reinforcement learning (RL), which is a subfield of machine learning (ML);
it … This can be addressed by reinforcement learning (RL), a subfield of ML, which is an …

Recent publications have laid a solid theoretical foundation for the combination of Reinforcement
Learning and Model Predictive Control, in view of obtaining high-performance data-…