RL development is being driven by several companies and research groups, including Google, Microsoft, and Facebook. It requires lots of investment in research, as there are not that many directions that are developed enough to be able to just take their methods and apply them to a problem. This is similar to how natural language processing and computer vision were several years ago. Having said that, the field of RL is attracting lots of attention, both from researchers and practitioners. This book helps readers to understand RL methods using real-life problems, and make the exciting RL domain accessible to a much wider audience than just research groups or large AI companies.

Reinforcement learning (RL) is a hugely popular area of deep learning, and many data scientists are exploring this AI technology to broaden their skillet to include a number of important problem domains like chatbots, robotics, discrete optimization, web automation and much more. As a result of this wide-spread interest in RL, there are many available educational resources specifically tailored to this class of deep learning – boot camps, training certificates, educational specializations, etc. But if you're a data scientist who has been programming in Python (with object oriented features) for a while, and has some experience with other forms of deep learning using a framework like TensorFlow, then maybe this new book, "Deep Reinforcement Learning Hands-On," by Maxim Lapan from Packt, might be a great way to kick-start yourself into becoming productive with RL. RL development is being driven by a number of large companies and research groups, including Google, Microsoft, and Facebook. RL requires considerable investment in research as the field is growing to enable data scientists to be able to take prescribed methods and apply them to a problem domain.

When I started learning RL three years ago, it was really hard to get practical information about the methods and ways that they could be implemented. Sparse blog posts about individual methods and theoretical papers, without code examples, were the only source of knowledge. To get something to experiment with, lots of time and effort was needed, fighting with weird bugs and misunderstanding mystic math in papers. With the rising popularity of RL, the situation has improved slightly, but, still, there is a lack of structured overview of the modern deep RL methods with a unified code base. This book fills the gap between theory and practice, providing a structured overview of recent RL methods, using clear examples written in uniform style.

In this article by Maxim Lapan, the author of Deep Reinforcement Learning Hands-On,we are going to discuss about gradients in PyTorch. Gradients support in tensors is one of the major changes in PyTorch 0.4.0. In previous versions, graph tracking and gradients accumulation were done in a separate, very thin class Variable, which worked as a wrapper around the tensor and automatically performed saving of the history of computations in order to be able to backpropagate. Now gradients are a built-in tensor property, which makes the API much cleaner. Gradient was originally implemented in the Caffe toolkit and then became the de-facto standard in DL libraries.