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What Is Deep Reinforcement Learning?

#artificialintelligence

Along with unsupervised machine learning and supervised learning, another common form of AI creation is reinforcement learning. Beyond regular reinforcement learning, deep reinforcement learning can lead to astonishingly impressive results, thanks to the fact that it combines the best aspects of both deep learning and reinforcement learning. Let's take a look at precisely how deep reinforcement learning operates. Note that this article won't delve too deeply into the formulas used in deep reinforcement learning, rather it aims to give the reader a high level intution for how the process works. Before we dive into deep reinforcement learning, it might be a good idea to refresh ourselves on how regular reinforcement learning works.


Demystifying deep reinforcement learning

#artificialintelligence

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Deep reinforcement learning is one of the most interesting branches of artificial intelligence. It is behind some of the most remarkable achievements of the AI community, including beating human champions at board and video games, self-driving cars, robotics, and AI hardware design. Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques. Deep reinforcement learning is much more complicated than the other branches of machine learning.


Demystifying deep reinforcement learning

#artificialintelligence

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Deep reinforcement learning is one of the most interesting branches of artificial intelligence. It is behind some of the most remarkable achievements of the AI community, including beating human champions at board and video games, self-driving cars, robotics, and AI hardware design. Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques. Deep reinforcement learning is much more complicated than the other branches of machine learning.


Debunking the mysteries of deep reinforcement learning

#artificialintelligence

Deep reinforcement learning is one of the most interesting branches ofartificial intelligence. It is behind some of the most remarkable achievements of the AI community, including beating human champions at board and video games, self-driving cars, robotics, and AI hardware design. Deep reinforcement learning leverages the learning capacity of deep neural networks to tackle problems that were too complex for classic RL techniques. Deep reinforcement learning is much more complicated than the other branches of machine learning. But in this post, I'll try to demystify it without going into the technical details.


A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

arXiv.org Artificial Intelligence

In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics and open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for readers from academia and industry into the topic and identifies notable opportunities for further research.