Applied Reinforcement Learning with Python, Taweh Beysolow II
Автор: Sutton, Richard S. Barto, Andrew G. Название: Reinforcement learning ISBN: 0262193981 ISBN-13(EAN): 9780262193986 Издательство: MIT Press Рейтинг: Цена: 66930.00 T Наличие на складе: Нет в наличии. Описание: An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field`s intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
Автор: Lapan Maxim Название: Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more ISBN: 1788834240 ISBN-13(EAN): 9781788834247 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging problems than ever before. Apply deep RL methods to training your agent to beat arcade ...
Автор: Ravichandiran Sudharsan Название: Hands-On Reinforcement Learning with Python ISBN: 1788836529 ISBN-13(EAN): 9781788836524 Издательство: Неизвестно Цена: 47810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Reinforcement 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.
Автор: Graesser Laura Harding, Wah Loon Keng Название: Deep Reinforcement Learning in Python: A Hands-On Introduction ISBN: 0135172381 ISBN-13(EAN): 9780135172384 Издательство: Pearson Education Рейтинг: Цена: 50150.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.
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 Proximal Policy Optimization (PPO)
Understand how algorithms can be parallelized synchronously and asynchronously
Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
Explore algorithm benchmark results with tuned hyperparameters
Understand how deep RL environments are designed
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. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Автор: Abhishek Nandy; Manisha Biswas Название: Reinforcement Learning ISBN: 1484232844 ISBN-13(EAN): 9781484232842 Издательство: Springer Рейтинг: Цена: 35390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: Reinforcement Learning basicsChapter Goal: This chapter covers the basics needed for AI, ML and Deep Learning.Relation between them and differences.No of pages 30Sub -Topics1. Reinforcement Learning2. The flow3. Faces of Reinforcement Learning4. 5. Environments6. The depiction of inter relation between Agents and EnvironmentDeep Learning Chapter 2: Theory and AlgorithmsChapter Goal: This Chapter covers the theory of Reinforcement Learning and Algorithms.No of pages: 60Sub-topics1 . Problem scenarios in Reinforcement Learningins 2. Markov Decision process3. SARSA4.Q learning5.Value Functions6.Dynamic Programming and Policies7.Approaches to RL Chapter 3: Open AI basicsChapter Goal: In this chapter we will cover the basics of Open AI gym and universe and then move forward for installing it. No of pages: 40 Sub - Topics: 1. What are Open AI environments 2. Installation of Open AI Gym and Universe in Ubuntu 3. Difference between Open AI Gym and Universe Chapter 4: Getting to know Open AI and Open AI gym the developers wayChapter Goal: We will use Python to start the programming and cover topics accordinglyNo of pages: 60Sub - Topics: 1. Open AI, Open AI Gym and python2. Setting up the environment3. Examples4 Swarm Intelligence using python 5.Markov Decision process toolbox for Python6.Implementing a Game AI with Reinforcement Learning Chapter 5: Reinforcement learning using Tensor Flow environment and KerasChapter Goal: We cover Reinforcement Learning in terms of Tensorflow and KerasNo of pages: 40Sub - Topics: 1. Tensorflow and Reinforcement Learning2. Q learning with Tensor Flow3. Keras4. Keras and Reinforcement Learning Chapter 6 Google's DeepMind and the future of Reinforcement LearningChapter Goal: We cover the descriptions of the above the content.No of pages: 25Sub - Topics: 1. Google's Deep Mind2. Future of Reinforcement Learning 3. Man VS Machines where is it Heading to.
Автор: Shimon Whiteson Название: Adaptive Representations for Reinforcement Learning ISBN: 3642422314 ISBN-13(EAN): 9783642422317 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presenting the main results of new algorithms for reinforcement learning, this book also introduces a novel method for devising input representations as well as presenting a way to find a minimal set of features sufficient to describe the agent`s current state.
Автор: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642101860 ISBN-13(EAN): 9783642101861 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.
Автор: Kathryn E. Merrick; Mary Lou Maher Название: Motivated Reinforcement Learning ISBN: 364210035X ISBN-13(EAN): 9783642100352 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended, virtual world.
Автор: Lapan Maxim Название: Deep Reinforcement Learning Hands-On - Second Edition ISBN: 1838826998 ISBN-13(EAN): 9781838826994 Издательство: Неизвестно Рейтинг: Цена: 98080.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With six new chapters, Deep Reinforcement Learning Hands-On Second edition is completely updated and expanded with the very latest reinforcement learning (RL) tools and techniques, providing you with an introduction to RL, as well as the hands-on ability to code intelligent learning agents to perform a range of practical tasks.
Автор: Christopher Gatti Название: Design of Experiments for Reinforcement Learning ISBN: 3319385518 ISBN-13(EAN): 9783319385518 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Автор: Yinyan Zhang; Shuai Li; Xuefeng Zhou Название: Deep Reinforcement Learning with Guaranteed Performance ISBN: 3030333833 ISBN-13(EAN): 9783030333836 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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 resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances.It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution.Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
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