Deep And Reinforcement Learning Networks And Methods
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Deep Reinforcement Learning Techniques And Fundamentals
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Author : Dr.S.Alex David
language : en
Publisher: SK Research Group of Companies
Release Date : 2023-10-03
Deep Reinforcement Learning Techniques And Fundamentals written by Dr.S.Alex David and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-03 with Computers categories.
Dr.S.Alex David, Professor, Department of Computer Science & Engineering, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Chennai, Tamil Nadu, India. Dr.Carmel Mary Belinda, Professor, Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu, India. Dr.Almas Begum, Professor, Department of Computer Science & Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu, India. Mrs.D.Hemalatha, Assistant Professor, Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India. Dr.S.Ravikumar, Associate Professor, Department of Computer Science & Engineering, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
Deep And Reinforcement Learning Networks And Methods
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Author : Mr.Chitra Sabapathy Ranganathan
language : en
Publisher: SK Research Group of Companies
Release Date : 2023-10-23
Deep And Reinforcement Learning Networks And Methods written by Mr.Chitra Sabapathy Ranganathan and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-23 with Computers categories.
Mr.Chitra Sabapathy Ranganathan, Associate Vice President, Mphasis Corporation, Arizona, USA
Deep Reinforcement Learning
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Author : Hao Dong
language : en
Publisher: Springer Nature
Release Date : 2020-06-29
Deep Reinforcement Learning written by Hao Dong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
Deep Reinforcement Learning In Action
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Author : Brandon Brown
language : en
Publisher: Simon and Schuster
Release Date : 2020-03-16
Deep Reinforcement Learning In Action written by Brandon Brown and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-16 with Computers categories.
Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. 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. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap
International Conference On Intelligent Emerging Methods Of Artificial Intelligence Cloud Computing
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Author : Fausto Pedro García Márquez
language : en
Publisher: Springer Nature
Release Date : 2022-05-04
International Conference On Intelligent Emerging Methods Of Artificial Intelligence Cloud Computing written by Fausto Pedro García Márquez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-04 with Technology & Engineering categories.
This book consists of different accepted papers of the conference. Firstly, the artificial intelligence and its application-related topics are provided. Secondly, cloud computing and related topics are also provided. The book has been designed to help research organisations and business leaders from across industries to transform their organisations into AI-driven disruptors. The utility of the technology in the face of massive globally interconnected complexity is explored. The significant characteristics of IEMAICLOUD are the promotion of inevitable dialogue between scientists, researchers, engineers, corporate’s and scholar’s students to mitigate the gap between academia, industry and governmental ethics which has been fostered through keynote speeches, workshops, panel discussion and oral presentations by eminent researchers in relevant field. The industry personnel depict cutting-edge researches in artificial intelligence and cloud computing to convey academia regarding real-time scenario and practical findings. Conference has been well equipped with talks by industry experts on the state of the art in computer science, lectures by eminent scientists designed to inspire and inform presentations by innovative researchers coming from 20+ countries from Europe and abroad. There has been discussion-oriented sessions and networking breaks to enable collaborations. Papers consist abstract, result, discussions and conclusions by the help of different tables and diagrams.
Deep Reinforcement Learning With Python
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Author : Sudharsan Ravichandiran
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-09-30
Deep Reinforcement Learning With Python written by Sudharsan Ravichandiran and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-30 with Mathematics categories.
An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook Description With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects. What you will learnUnderstand core RL concepts including the methodologies, math, and codeTrain an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI GymTrain an agent to play Ms Pac-Man using a Deep Q NetworkLearn policy-based, value-based, and actor-critic methodsMaster the math behind DDPG, TD3, TRPO, PPO, and many othersExplore new avenues such as the distributional RL, meta RL, and inverse RLUse Stable Baselines to train an agent to walk and play Atari gamesWho this book is for If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.
Deep Reinforcement Learning Hands On
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Author : Maxim Lapan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-31
Deep Reinforcement Learning Hands On written by Maxim Lapan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-31 with Computers categories.
Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods Apply RL methods to cheap hardware robotics platforms Book DescriptionDeep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.What you will learn Understand the deep learning context of RL and implement complex deep learning models Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft s TextWorld environment, which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik s Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover advanced exploration techniques, including noisy networks and network distillation techniques Who this book is for Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL
The Proceedings Of The 2021 Asia Pacific International Symposium On Aerospace Technology Apisat 2021 Volume 2
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Author : Sangchul Lee
language : en
Publisher: Springer Nature
Release Date : 2022-09-29
The Proceedings Of The 2021 Asia Pacific International Symposium On Aerospace Technology Apisat 2021 Volume 2 written by Sangchul Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-29 with Technology & Engineering categories.
This proceeding comprises peer-reviewed papers of the 2021 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2021), held from 15-17 November 2021 in Jeju, South Korea. This book deals with various themes on computational fluid dynamics, wind tunnel testing, flow visualization, UAV design, flight simulation, satellite attitude control, aeroelasticity and control, combustion analysis, fuel injection, cooling systems, spacecraft propulsion and so forth. So, this book can be very helpful not only for the researchers of universities and academic institutes, but also for the industry engineers who are interested in the current and future advanced topics in aerospace technology.
The The Reinforcement Learning Workshop
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Author : Alessandro Palmas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-08-18
The The Reinforcement Learning Workshop written by Alessandro Palmas and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-18 with Computers categories.
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
Deep Learning
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Author : Manish Soni
language : en
Publisher:
Release Date : 2024-11-13
Deep Learning written by Manish Soni and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-13 with Computers categories.
Welcome to "Deep Learning: A Comprehensive Guide," a book meticulously designed to cater to the needs of learners at various stages of their journey into the fascinating world of deep learning. Whether you are a beginner embarking on your first exploration into artificial intelligence or a seasoned professional looking to deepen your expertise, this book aims to be your trusted companion. Deep learning, a subset of machine learning, has revolutionized the field of artificial intelligence, enabling advancements that were once thought to be the stuff of science fiction. From autonomous vehicles to sophisticated natural language processing systems, deep learning has become the backbone of many cutting-edge technologies. Understanding and mastering deep learning is not just a desirable skill but a necessity for anyone looking to thrive in the modern tech landscape. What This Book Offers This book is not just a theoretical exposition but a practical guide designed to provide you with a holistic learning experience. Here's a glimpse of what you can expect: Structured Content: Starts with neural network basics and advances to topics like convolutional, recurrent, and generative adversarial networks. Each chapter builds on the previous, ensuring a comprehensive learning journey. Online Practice Questions: Each chapter includes practice questions from basic to advanced levels to test and reinforce your understanding. Videos: Instructional videos complement the book's content, offering step-by-step explanations and real-life applications. Exercises and Projects: Includes exercises and hands-on projects that simulate real-world problems, providing practical experience. Lab Activities: Features lab activities using frameworks like TensorFlow and PyTorch for hands-on experimentation with deep learning models. Case Studies: Illustrates the application of deep learning in industries such as healthcare, finance, and entertainment, highlighting its transformative potential. Comprehensive Coverage: Covers a broad spectrum of topics, from theoretical foundations to practical implementations, latest advancements, ethical considerations, and future trends. Who Should Use This Book? This book is designed for: Students and Academics: Pursuing studies in computer science, data science, or related fields. Industry Professionals: Enhancing skills or transitioning into roles involving deep learning. Embarking on the journey to master deep learning is both challenging and rewarding. This book is designed to make that journey as smooth and enlightening as possible. We hope that the combination of theoretical knowledge, practical exercises, projects, and real-world applications will equip you with the skills and confidence needed to excel in the field of deep learning.