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Handbook of Reinforcement Learning and Control

This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory;

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Human-Robot Interaction Control Using Reinforcemen t Learning

, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control;

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Learning Control

, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications;

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Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure;

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Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure;

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Reinforcement Learning

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational;

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Statistical Reinforcement Learning

field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms;

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Statistical Reinforcement Learning

field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms;

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Reinforcement Learning Algorithms

learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special;

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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and;

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Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling

job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore;

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Reinforcement Learning

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve;

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Applied Reinforcement Learning with Python

Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important;

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Python Reinforcement Learning Projects

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Key Features Implement Q-learning;

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Reconstruction and Intelligent Control for Power Plant

, deep reinforcement learning, and networked collaborative control. It could help researchers, industrial engineers, and graduate students in the;

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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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Grokking Deep Reinforcement Learning

. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal;

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Handbook of Composite Reinforcements

in one accessible and informative handbook. Fibers, processes, and composite reinforcement types, as well as relevant miscellaneous subjects such;

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Neural Fuzzy Control Systems With Structure And Parameter Learning

fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a;

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Neural Basis of Motivational and Cognitive Control

developments in computational approaches, including reinforcement learning; and overarching trends in the current literature, including neuroeconomics;

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Deep Reinforcement Learning with Python

, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding;

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Practical MATLAB Deep Learning

deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to;

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Practical MATLAB Deep Learning

deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to;

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Hands-On Reinforcement Learning with Python

advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and;

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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

engineered systems. ADP is a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems. It;

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Reinforcement Learning

Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their;

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TensorFlow 2 Reinforcement Learning Cookbook

Features Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services Explore popular;

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