Reinforcement learning (RL) has emerged as a powerful paradigm for training autonomous
software agents to make decisions in complex and dynamic environments. This abstract …

Abstract Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical
systems for automation tasks. It is important to record the developing trends in DRL's …

A hands-on guide enriched with examples to master deep reinforcement learning algorithms
with Python Key Features Your entry point into the world of artificial intelligence using the …

Abstract The combination of Reinforcement Learning (RL) with deep learning has led to a
series of impressive feats, with many believing (deep) RL provides a path towards generally …

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 …

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL)
for computer systems. Using RL for improving the performance of systems has a lot of …

Deep reinforcement learning (DRL) combines deep learning (DL) with a reinforcement
learning (RL) architecture. It has been able to perform a wide range of complex decision …

In recent trends, artificial intelligence (AI) is used for the creation of complex automated
control systems. Still, researchers are trying to make a completely autonomous system that …

Reinforcement learning is a mathematical framework for developing computer agents that
can learn an optimal behavior by relating generic reward signals with its past actions. With …

Reinforcement learning (RL) algorithms have been successfully applied to a range of
challenging sequential decision‐making and control tasks. In this paper, we classify RL into …