Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) which focuses on
training agents to make decisions by interacting with their environment to maximize …

Reinforcement learning (RL) is a dynamic and evolving subfield of machine learning that
focuses on training intelligent agents to learn and adapt through interactions with their …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

In reinforcement learning (RL), different rewards can define the same optimal policy but
result in drastically different learning performance. For some, the agent gets stuck with a …

Reinforcement learning (RL) has attracted rapidly increasing interest in the machine
learning and artificial intelligence communities in the past decade. With tremendous success …

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This
book brings together many different aspects of the current research on several fields …

In this chapter, we introduce and summarize the taxonomy and categories for reinforcement
learning (RL) algorithms. Figure 3.1 presents an overview of the typical and popular …

This article is a gentle discussion about the field of reinforcement learning in practice, about
opportunities and challenges, touching a broad range of topics, with perspectives and …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …