… This paper surveys the field of reinforcement learning from a … learning. Both the historical
basis of the field and a broad selection of current work are summarized. Reinforcement learning …

… , such as learning to play video … survey, we begin with an introduction to the general field
of RL, then progress to the main streams of value-based and policy-based methods. Our survey …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

… In addition, deep learning has stimulated the further development of many … reinforcement
learning, such as hierarchical reinforcement learning (HRL), multiagent reinforcement learning, …

… approaches that have been applied to deep reinforcement learning. Note that this review is
… reinforcement learning’ as it is a more general term rather than ’deep reinforcement learning…

… Reinforcement learning … reinforcement learning techniques for tackling dynamically changing
environment contexts in a system. The focus is on a single autonomous RL agent learning …

… detail in this survey are temporal differences, Q-learning, semi-… The survey points to more
than 100 references from the … In this survey, we will discuss the main ideas in RL, with special …

… In this survey, we begin with an introduction to the general field of reinforcement learning,
then progress to the main … Our survey will cover central algorithms in deep reinforcement …

… Therefore, this survey presents a combination of planning and learning. A general scheme
… and learning, which we will use throughout the survey, is shown in Figure 1.1. The survey is …

… However, this is not the case with reinforcement learning (… learning (LeCun et al. 2015).
Thus, the main goal of this article is to provide a review of applications of reinforcement learning …