Описание: Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman’s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on.
Автор: Dutta Sayon Название: Reinforcement Learning with Tensorflow ISBN: 1788835727 ISBN-13(EAN): 9781788835725 Издательство: Неизвестно Цена: 10666.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reinforcement learning allows you to develop intelligent, self-learning systems. This book shows you how to put the concepts of Reinforcement Learning to train efficient models.You will use popular reinforcement learning algorithms to implement use-cases in image processing and NLP, by combining the power of TensorFlow and OpenAI Gym.
Автор: Ahlawat Название: Reinforcement Learning for Finance ISBN: 1484288343 ISBN-13(EAN): 9781484288344 Издательство: Springer Рейтинг: Цена: 4268.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN – two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, and loss functions. After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library. What You Will Learn * Understand the fundamentals of reinforcement learning * Apply reinforcement learning programming techniques to solve quantitative-finance problems * Gain insight into convolutional neural networks and recurrent neural networks * Understand the Markov decision process Who This Book Is For Data Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.
Автор: Ciaburro Giuseppe Название: Keras Reinforcement Learning Projects ISBN: 1789342090 ISBN-13(EAN): 9781789342093 Издательство: Неизвестно Рейтинг: Цена: 10666.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Keras Reinforcement Learning Projects book teaches you essential concept, techniques and, models of reinforcement learning using best real-world demonstrations. You will explore popular algorithms such as Markov decision process, Monte Carlo, Q-learning making you equipped with complex statistics in various projects with the help of Keras
Название: Recent advances in reinforcement learning ISBN: 0792397053 ISBN-13(EAN): 9780792397052 Издательство: Springer Рейтинг: Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Addresses research in the Artificial Intelligence and Neural Network communities. This book includes topics such as the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques.
Автор: Leno da Silva, Felipe Costa, Anna Helena Reali Название: Transfer Learning for Multiagent Reinforcement Learning Systems ISBN: 3031004639 ISBN-13(EAN): 9783031004636 Издательство: Springer Рейтинг: Цена: 7317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning.This book surveys the literature on knowledge reuse in multiagent RL.
Автор: Schwartz H M Название: Multi-Agent Machine Learning ISBN: 111836208X ISBN-13(EAN): 9781118362082 Издательство: Wiley Рейтинг: Цена: 15198.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces.
Автор: Ravichandiran, Sudharsan Название: Hands-on reinforcement learning with python - ISBN: 1839210680 ISBN-13(EAN): 9781839210686 Издательство: Неизвестно Рейтинг: Цена: 9378.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures - including deep reinforcement learning - from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning.
Автор: Sugiyama, Masashi Название: Statistical Reinforcement Learning ISBN: 0367575868 ISBN-13(EAN): 9780367575861 Издательство: Taylor&Francis Рейтинг: Цена: 7348.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Da Silva Felipe Leno, Reali Costa Anna Helena Название: Transfer Learning for Multiagent Reinforcement Learning Systems ISBN: 1636391346 ISBN-13(EAN): 9781636391342 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 8039.00 р. Наличие на складе: Поставка под заказ.
Описание:
Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment.
However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning.
This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools.
This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.
Автор: Michael C. Fu, Prashanth L. A. Название: Risk-Sensitive Reinforcement Learning Via Policy Gradient Search ISBN: 1638280266 ISBN-13(EAN): 9781638280262 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 14414.00 р. Наличие на складе: Поставка под заказ.
Описание: Reinforcement learning (RL) is one of the foundational pillars of artificial intelligence and machine learning. An important consideration in any optimization or control problem is the notion of risk, but its incorporation into RL has been a recent development. This monograph surveys research on risk-sensitive RL that uses policy gradient search.
Автор: Balakrishnan Kaushik Название: TensorFlow Reinforcement Learning Quick Start Guide ISBN: 1789533589 ISBN-13(EAN): 9781789533583 Издательство: Неизвестно Рейтинг: Цена: 5148.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and ...
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