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Book Review: Foundations of Deep Reinforcement Learning, by Laura Graesser and Wah Loon Keng

Deep Reinforcement Learning is a somewhat new field within Machine Learning or Artificial Intelligence (you may pick your favorite term between these two, even if they’re not strictly the same), which combines Deep Learning and Reinforcement Learning and is based on the general idea that an agent can learn by observing its actions and their consequences. No, it is not a return to John B. Watson and B. F. Skinner’s behavioral psychology. We are talking, instead, about a set of pretty advanced machine learning algorithms that, when properly mastered, allow computers to achieve remarkable results in some complex tasks. That’s what this book is about, so let’s dive in…

Book Structure and Contents

Foundations of Deep Reinforcement Learning - Theory and Practice in Python begins with a brief preliminary chapter, which serves to introduce a few concepts and terms that will be used throughout all the other chapters: agent, state, action, objective, reward, reinforcement, policy, value function, model, trajectory, transition.

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What You Need to Know About Deep Reinforcement Learning

Machine learning (ML) and artificial intelligence (AI) algorithms are increasingly powering our modern society and leaving their mark on everything from finance, healthcare, to transportation. If the late half of the 20th century was about the general progress in computing and connectivity (internet infrastructure), the 21st century is shaping up to be dominated by intelligent computing and a race toward smarter machines.

Most of the discussion and awareness about these novel computing paradigms, however, circle around the so-called ‘supervised learning’, in which deep learning (DL) occupies a central position. The recent advancement and astounding success of deep neural networks (DNN) – from disease classification to image segmentation to speech recognition – has led to much excitement and application of DNNs in all facets of high-tech systems. 

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2 Things You Need to Know About Reinforcement Learning: Computational and Sample Efficiency

The High Cost of Deep Learning

Have you ever put on a sweater because the air conditioning was too cold? Forgotten to turn off the lights in another room before heading to bed? Do you commute to work more than 30 minutes every day just for the sake of “filling seats” at the office, even though everything you do at work could be done via laptop from home? 

In the counter-intuitive trade-offs between sample and computational efficiency in Reinforcement Learning, choosing evolution strategies can be smarter than it looks.

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Trading Strategies Using Deep Reinforcement Learning

The purpose of this post is to expose some results after creating a trading bot based on Reinforcement Learning that is capable of generating a trading strategy and at the same time to share a possible architecture for an agent and the features of the dataset that was used, furthermore to share detail about the problems faced.

First, we need to understand the problem, so let’s talk about Trading.

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Deep Reinforced Learning: Addressing Complex Enterprise Challenges

Current deep learning algorithms and methods are nowhere near the holy grail of “Artificial General Intelligence (AGI).”

Current algorithms lean more towards narrow learning, meaning they are good at learning and solving specific types of problems under specific conditions. These algorithms take a humongous amount of data as compared to humans who can learn from relatively few learning encounters. The transfer process of these learnings from one problem domain to another domain is somewhat limited as well.

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Intro to Machine Learning for Developers

Welcome to the world of machine learning with scikit-learn. Machine learning can be overwhelming at times, and this is partly due to a large number of tools that are available on the market. This post will simplify this process of tool selection down to one — scikit-learn.

In this series, you will learn how to construct an end-to-end machine learning pipeline using some of the most popular algorithms that are widely used in industry and professional competitions, such as Kaggle.