Found insideThis book is an outgrowth of a 1996 NIPS workshop called Tricks of the Trade whose goal was to begin the process of gathering and documenting these tricks. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. Found insideThis book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields. Computer networks and reinforcement learning algorithms have substantially advanced over the past decade. Game developers are being challenged to enlist cutting edge AI as part of their games. In this book, you will look at the journey of building capable AI using reinforcement learning algorithms and techniques. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. With this book, you'll learn how to implement reinforcement learning with R, exploring practical examples such as using tabular Q-learning to control robots. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. This book will help you master RL algorithms and understand their implementation as you build self-learning agents. Found insideThis book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. Found insideThis book shows you how to build predictive models, detect anomalies, analyze text and images, and more. Machine learning makes all this possible. Dive into this exciting new technology with Machine Learning For Dummies, 2nd Edition. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. This book will show you how to take advantage of TensorFlow’s most appealing features - simplicity, efficiency, and flexibility - in various scenarios. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Found insideAI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. Found insideTopics included in this book are: How to access SAS OnDemand for Academics Descriptive statistics One-sample tests T tests (for independent or paired samples) One-way analysis of variance (ANOVA) N-way ANOVA Correlation analysis Simple and ... This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Found insideThis book is about making machine learning models and their decisions interpretable. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive ... Most of the entries in this preeminent work include useful literature references. Found inside – Page iThis book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances ... Found insidePython Reinforcement Learning Projects brings various aspects and methodologies of RL using 8 real-world projects that explore RL and will have hands-on experience with real data and artificial intelligence problems. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning’s core techniques and frameworks. This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology ... Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Found insideThe cross-entropy (CE) method is one of the most significant developments in stochastic optimization and simulation in recent years. This book explains in detail how and why the CE method works. Found insideThis practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement ... This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy ... Found insideThis book will help you master RL algorithms and understand their implementation as you build self-learning agents. 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. Found insideReinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python. This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and ... Found insideThis book brings all these topics under one roof and discusses their similarities and differences. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and ... Found inside – Page iA comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible ... Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains. Found insideThis 2 volume-set of IFIP AICT 583 and 584 constitutes the refereed proceedings of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, held in Neos Marmaras, Greece, in June ... 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