This paper aims to review the advancements, limitations, and real-world applications of RL.
Additionally, it will explore the future of RL and the challenges that must be addressed to …

Reinforcement learning (RL) has become more popular due to promising results in
applications such as chat-bots, healthcare, and autonomous driving. However, one …

Reinforcement learning (RL) is a highly promising yet challenging subfield of artificial
intelligence (AI) that plays a crucial role in shaping the future of intelligent systems. From …

Reinforcement learning (RL) is a pivotal branch of machine learning focused on training
agents to make sequences of decisions by maximizing cumulative rewards in dynamic …

This thesis explores the challenge of making reinforcement learning (RL) more suitable to
real-world problems without loosing theoretical guarantees. This is an interesting active …

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 …

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 …

Reinforcement learning (RL) has demonstrated great potential, but is currently full of
overhyping and pipe dreams. We point to some difficulties with current research which we …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Reinforcement Learning (RL) has continuously risen in popularity in recent years.
Consequently, multiple RL algorithms and extensions have been developed for various use …