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• The Cognitive Dialogue: A New Architecture for Perception and Cognition.- Rooftop-Aware Emergency Landing Planning for Small Unmanned Aircraft Systems.- Quantum Reinforcement Learning in Changing Environment.- The Role of Thermodynamics in the Future Research Directions in Control and Learning.- Mixed Density Reinforcement Learning Methods for Approximate Dynamic Programming.- Analyzing and Mitigating Link-Flooding DoS Attacks Using Stackelberg Games and Adaptive Learning.- Learning and Decision Making for Complex Systems Subjected to Uncertainties: A Stochastic Distribution Control Approach.- Optimal Adaptive Control of Partially Unknown Linear Continuous-time Systems with Input and State Delay.- Gradient Methods Solve the Linear Quadratic Regulator Problem Exponentially Fast.- Architectures, Data Representations and Learning Algorithms: New Directions at the Confluence of Control and Learning.- Reinforcement Learning for Optimal Feedback Control and Multiplayer Games.- Fundamental Principles of Design for Reinforcement Learning Algorithms Course Titles.- Long-Term Impacts of Fair Machine Learning.- Learning-based Model Reduction for Partial Differential Equations with Applications to Thermo-Fluid Models' Identification, State Estimation, and Stabilization.- CESMA: Centralized Expert Supervises Multi-Agents, for Decentralization.- A Unified Framework for Reinforcement Learning and Sequential Decision Analytics.- Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration-Exploitation Dilemma in Reinforcement Learning.- Multi-Agent Reinforcement Learning: Recent Advances, Challenges, and Applications.- Reinforcement Learning Applications, An Industrial Perspective.- A Hybrid Dynamical Systems Perspective of Reinforcement Learning.- Bounded Rationality and Computability Issues in Learning, Perception, Decision-Making, and Games Panagiotis Tsiotras.- Mixed Modality Learning.- Computational Intelligence in Uncertainty Quantification for Learning Control and Games.- Reinforcement Learning Based Optimal Stabilization of Unknown Time Delay Systems Using State and Output Feedback.- Robust Autonomous Driving with Humans in the Loop.- Boundedly Rational Reinforcement Learning for Secure Control.
• (source: Nielsen Book Data)

This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative. .
(source: Nielsen Book Data)

• Introduction.- Reinforcement Learning Background.- RelatedWork.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future work.
• (source: Nielsen Book Data)

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research. The key contributions of this book are: Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In-depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read. Peter Stone, Associate Professor of Computer Science

• Preface Introduction Powertrain Modeling and Reinforcement Learning Prediction and Updating of Driving Information Evaluation of Intelligent Energy Management System Conclusion References Author's Biography.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Prediction Error and Actor-Critic Hypotheses in the Brain.- Reviewing on-policy / off-policy critic learning in the context of Temporal Differences and Residual Learning.- Reward Function Design in Reinforcement Learning.- Exploration Methods In Sparse Reward Environments.- A Survey on Constraining Policy Updates Using the KL Divergence.- Fisher Information Approximations in Policy Gradient Methods.- Benchmarking the Natural gradient in Policy Gradient Methods and Evolution Strategies.- Information-Loss-Bounded Policy Optimization.- Persistent Homology for Dimensionality Reduction.- Model-free Deep Reinforcement Learning - Algorithms and Applications.- Actor vs Critic.- Bring Color to Deep Q-Networks.- Distributed Methods for Reinforcement Learning.- Model-Based Reinforcement Learning.- Challenges of Model Predictive Control in a Black Box Environment.- Control as Inference?
• (source: Nielsen Book Data)

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 and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universitat Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction Bayesian Bandit Model and Gittins Index Variants of the Bayesian Bandit Model Frequentist Bandit Model Variants of the Frequentist Bandit Model Application Examples Bibliography Author's Biography.
• (source: Nielsen Book Data)

Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. We start in Chapter 1 with a brief overview on the history of bandit problems, contrasting the two schools-Bayesian and frequentis -of approaches and highlighting foundational results and key applications. Chapters 2 and 4 cover, respectively, the canonical Bayesian and frequentist bandit models. In Chapters 3 and 5, we discuss major variants of the canonical bandit models that lead to new directions, bring in new techniques, and broaden the applications of this classical problem. In Chapter 6, we present several representative application examples in communication networks and social-economic systems, aiming to illuminate the connections between the Bayesian and the frequentist formulations of bandit problems and how structural results pertaining to one may be leveraged to obtain solutions under the other.
(source: Nielsen Book Data)

• Table of Contents Overview of Keras Reinforcement Learning Simulating random walks Optimal Portfolio Selection Forecasting stock market prices Delivery Vehicle Routing Application Prediction and Betting Evaluations of coin flips using Markov decision processes Build an optimized vending machine using Dynamic Programming Robot control system using Deep Reinforcement Learning Handwritten Digit Recognizer Playing the board game Go What is next?
• (source: Nielsen Book Data)

A practical guide to mastering reinforcement learning algorithms using Keras Key Features Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action Get to grips with Keras and practice on real-world unstructured datasets Uncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning Book DescriptionReinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You'll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You'll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you've understood the basics, you'll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you'll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you'll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI. What you will learn Practice the Markov decision process in prediction and betting evaluations Implement Monte Carlo methods to forecast environment behaviors Explore TD learning algorithms to manage warehouse operations Construct a Deep Q-Network using Python and Keras to control robot movements Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset Address a game theory problem using Q-Learning and OpenAI Gym Who this book is forKeras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book.
(source: Nielsen Book Data)

• 1. Build TensorFlow Input Pipeline
• s2. Increase the Diversity of your Dataset with Data Augmentatio
• n3. TensorFlow Dataset
• s4. Deep Learning with TensorFlow Dataset
• s5. Introduction to Tensor Processing Unit
• s6. Simple Transfer Learning with TensorFlow Hu
• b7. Advanced Transfer Learnin
• g8. Stacked Autoencoder
• s9. Convolutional and Variational Autoencoder
• s10. Generative Adversarial Network
• s11. Progressive Growing Generative Adversarial Network
• s12. Fast Style Transfe
• r13. Object Detectio
• n14. An Introduction to Reinforcement Learning.
• (source: Nielsen Book Data)

Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning. Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office. What You Will Learn Take advantage of the built-in support of the Google Colab ecosystem Work with TensorFlow data sets Create input pipelines to feed state-of-the-art deep learning models Create pipelined state-of-the-art deep learning models with clean and reliable Python code Leverage pre-trained deep learning models to solve complex machine learning tasks Create a simple environment to teach an intelligent agent to make automated decisions Who This Book Is For Readers who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab.
(source: Nielsen Book Data)

• Introduction to Reinforcement Learning. Model-Free Policy Iteration. Policy Iteration with Value Function Approximation. Basis Design for Value Function Approximation. Sample Reuse in Policy Iteration. Active Learning in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement Learning. Transition Model Estimation. Dimensionality Reduction for Transition Model Estimation.
• (source: Nielsen Book Data)

• Introduction to Reinforcement Learning Reinforcement Learning Mathematical Formulation Structure of the Book
• Model-Free Policy Iteration
• Model-Free Policy Search
• Model-Based Reinforcement Learning MODEL-FREE POLICY ITERATION Policy Iteration with Value Function Approximation Value Functions
• State Value Functions
• State-Action Value Functions Least-Squares Policy Iteration
• Immediate-Reward Regression
• Algorithm
• Regularization
• Model Selection Remarks Basis Design for Value Function Approximation Gaussian Kernels on Graphs
• MDP-Induced Graph
• Ordinary Gaussian Kernels
• Geodesic Gaussian Kernels
• Extension to Continuous State Spaces Illustration
• Setup
• Geodesic Gaussian Kernels
• Ordinary Gaussian Kernels
• Graph-Laplacian Eigenbases
• Diffusion Wavelets Numerical Examples
• Robot-Arm Control
• Robot-Agent Navigation Remarks Sample Reuse in Policy Iteration Formulation Off-Policy Value Function Approximation
• Episodic Importance Weighting
• Per-Decision Importance Weighting
• Adaptive Per-Decision Importance Weighting
• Illustration Automatic Selection of Flattening Parameter
• Importance-Weighted Cross-Validation
• Illustration Sample-Reuse Policy Iteration
• Algorithm
• Illustration Numerical Examples
• Inverted Pendulum
• Mountain Car Remarks Active Learning in Policy Iteration Efficient Exploration with Active Learning
• Problem Setup
• Decomposition of Generalization Error
• Estimation of Generalization Error
• Designing Sampling Policies
• Illustration Active Policy Iteration
• Sample-Reuse Policy Iteration with Active Learning
• Illustration Numerical Examples Remarks Robust Policy Iteration Robustness and Reliability in Policy Iteration
• Robustness
• Reliability Least Absolute Policy Iteration
• Algorithm
• Illustration
• Properties Numerical Examples Possible Extensions
• Huber Loss
• Pinball Loss
• Deadzone-Linear Loss
• Chebyshev Approximation
• Conditional Value-At-Risk Remarks MODEL-FREE POLICY SEARCH Direct Policy Search by Gradient Ascent Formulation Gradient Approach
• Gradient Ascent
• Baseline Subtraction for Variance Reduction
• Variance Analysis of Gradient Estimators Natural Gradient Approach
• Natural Gradient Ascent
• Illustration Application in Computer Graphics: Artist Agent
• Sumie Paining
• Design of States, Actions, and Immediate Rewards
• Experimental Results Remarks Direct Policy Search by Expectation-Maximization Expectation-Maximization Approach Sample Reuse
• Episodic Importance Weighting
• Per-Decision Importance Weight
• Adaptive Per-Decision Importance Weighting
• Automatic Selection of Flattening Parameter
• Reward-Weighted Regression with Sample Reuse Numerical Examples Remarks Policy-Prior Search Formulation Policy Gradients with Parameter-Based Exploration
• Policy-Prior Gradient Ascent
• Baseline Subtraction for Variance Reduction
• Variance Analysis of Gradient Estimators
• Numerical Examples Sample Reuse in Policy-Prior Search
• Importance Weighting
• Variance Reduction by Baseline Subtraction
• Numerical Examples Remarks MODEL-BASED REINFORCEMENT LEARNING Transition Model Estimation Conditional Density Estimation
• Regression-Based Approach
• Q-Neighbor Kernel Density Estimation
• Least-Squares Conditional Density Estimation Model-Based Reinforcement Learning Numerical Examples
• Continuous Chain Walk
• Humanoid Robot Control Remarks Dimensionality Reduction for Transition Model Estimation Sufficient Dimensionality Reduction Squared-Loss Conditional Entropy
• Conditional Independence
• Dimensionality Reduction with SCE
• Relation to Squared-Loss Mutual Information Numerical Examples
• Artificial and Benchmark Datasets
• Humanoid Robot Remarks References Index.
• (source: Nielsen Book Data)

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. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amo.
(source: Nielsen Book Data)
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. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspective Lays out the associated optimization problems for each reinforcement learning scenario covered Provides thought-provoking statistical treatment of reinforcement learning algorithms The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. 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.
(source: Nielsen Book Data)

• Introduction.- The basics of feedback control systems.- Reinforcement learning and feedback control.- Q-learning aided performance optimization of deterministic systems.- NAC aided performance optimization of stochastic systems.- Conclusion and future work.
• (source: Nielsen Book Data)

Changsheng Hua proposes two approaches, an input/output recovery approach and a performance index-based approach for robustness and performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author develops Q-learning and natural actor-critic (NAC) methods, respectively. Their effectiveness has been demonstrated by an experimental study on a brushless direct current motor test rig. The author: Changsheng Hua received the Ph.D. degree at the Institute of Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany, in 2020. His research interests include model-based and data-driven fault diagnosis and fault-tolerant techniques.
(source: Nielsen Book Data)

• 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.
• (source: Nielsen Book Data)

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You'll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov's Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You'll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There's also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google's Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn Absorb the core concepts of the reinforcement learning process Use advanced topics of deep learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book Is For Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.
(source: Nielsen Book Data)

• Chapter 1: Introduction to Deep Reinforcement LearningChapter Goal: Introduce the reader to field of reinforcement learning and setting the context of what they will learn in rest of the bookSub -Topics1. Deep reinforcement learning2. Examples and case studies3. Types of algorithms with mind-map4. Libraries and environment setup5. Summary
• Chapter 2: Markov Decision ProcessesChapter Goal: Help the reader understand models, foundations on which all algorithms are built. Sub - Topics 1. Agent and environment2. Rewards3. Markov reward and decision processes4. Policies and value functions5. Bellman equations
• Chapter 3: Model Based Algorithms Chapter Goal: Introduce reader to dynamic programming and related algorithms Sub - Topics: 1. Introduction to OpenAI Gym environment2. Policy evaluation/prediction3. Policy iteration and improvement4. Generalised policy iteration5. Value iteration
• Chapter 4: Model Free ApproachesChapter Goal: Introduce Reader to model free methods which form the basis for majority of current solutionsSub - Topics: 1. Prediction and control with Monte Carlo methods2. Exploration vs exploitation3. TD learning methods4. TD control5. On policy learning using SARSA6. Off policy learning using q-learning
• Chapter 5: Function Approximation Chapter Goal: Help readers understand value function approximation and Deep Learning use in Reinforcement Learning. 1. Limitations to tabular methods studied so far2. Value function approximation3. Linear methods and features used4. Non linear function approximation using deep Learning
• Chapter 6: Deep Q-Learning Chapter Goal: Help readers understand core use of deep learning in reinforcement learning. Deep q learning and many of its variants are introduced here with in depth code exercises. 1. Deep q-networks (DQN)2. Issues in Naive DQN 3. Introduce experience replay and target networks4. Double q-learning (DDQN)5. Duelling DQN6. Categorical 51-atom DQN (C51)7. Quantile regression DQN (QR-DQN)8. Hindsight experience replay (HER)
• Chapter 7: Policy Gradient Algorithms Chapter Goal: Introduce reader to concept of policy gradients and related theory. Gain in depth knowledge of common policy gradient methods through hands-on exercises1. Policy gradient approach and its advantages2. The policy gradient theorem3. REINFORCE algorithm4. REINFORCE with baseline5. Actor-critic methods6. Advantage actor critic (A2C/A3C)7. Proximal policy optimization (PPO)8. Trust region policy optimization (TRPO)
• Chapter 8: Combining Policy Gradients and Q-Learning Chapter Goal: Introduce reader to the trade offs between two approaches ways to connect together the two seemingly dissimilar approaches. Gain in depth knowledge of some land mark approaches.1. Tradeoff between policy gradients and q-learning2. The connection3. Deep deterministic policy gradient (DDPG)4. Twin delayed DDPG (TD3)5. Soft actor critic (SAC)
• Chapter 9: Integrated Learning and Planning Chapter Goal: Introduce reader to the scalable approaches which are sample efficient for scalable problems.1. Model based reinforcement learning2. Dyna and its variants3. Guided policy search4. Monte Carlo tree search (MCTS)5. AlphaGo
• Chapter 10: Further Exploration and Next Steps Chapter Goal: With the backdrop of having gone through most of the popular algorithms, readers are now introduced again to exploration vs exploitation dilemma, central to reinforcement learning. 1. Multi arm bandits2. Upper confidence bound3. Thompson sampling.
• (source: Nielsen Book Data)

Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods. You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym. What You'll Learn Examine deep reinforcement learning Implement deep learning algorithms using OpenAI's Gym environment Code your own game playing agents for Atari using actor-critic algorithms Apply best practices for model building and algorithm training Who This Book Is For Machine learning developers and architects who want to stay ahead of the curve in the field of AI and deep learning.
(source: Nielsen Book Data)

• Chapter 1: Introduction to Reinforcement LearningChapter Goal: Inform the reader of the history of the field, its current applications, as well as generally discussing the outline of the text and what the reader can expect to learn No of pages 10Sub -Topics1. What is reinforcement learning? 2. History of reinforcement learning 3. Applications of reinforcement learning
• Chapter 2: Reinforcement Learning AlgorithmsChapter Goal: Establishing an understanding with the reader about how reinforcement learning algorithms work and how they differ from basic ML/DL methods. Practical examples to be provided for this chapter No of pages: 50 Sub - Topics 1. Tabular solution methods2. Approximate solution methods
• Chapter 3: Q Learning Chapter Goal: In this chapter, readers will continue to build on their understanding of RL by solving problems in discrete action spaces No of pages : 40 Sub - Topics: 1. Deep Q networks2. Double deep Q learning
• Chapter 4: Reinforcement Learning Based Market Making Chapter Goal: In this chapter, we will focus on a financial based use case, specifically market making, in which we must buy and sell a financial instrument at any given price. We will apply a reinforcement learning approach to this data set and see how it performs over time No of pages: 50Sub - Topics: 1. Market making 2. AWS/Google Cloud3. Cron
• Chapter 5: Reinforcement Learning for Video Games Chapter Goal: In this chapter, we will focus on a more generalized use case of reinforcement learning in which we teach an algorithm to successfully play a game against computer based AI. No of pages: 50Sub - Topics: 1. Game background and data collection .
• (source: Nielsen Book Data)

Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll Learn Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and Keras Deploy and train reinforcement learning-based solutions via cloud resources Apply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
(source: Nielsen Book Data)

• Section 1 - Rule Discovery. Population Dynamics of Genetic Algorithms. Approximating Value Functions in Classifier Systems. Two Simple Learning Classifier Systems. Computational Complexity of the XCS Classifier System. An Analysis of Continuous-Valued Representations for Learning Classifier Systems.-
• Section 2 - Credit Assignment. Reinforcement Learning: a Brief Overview. A Mathematical Framework for Studying Learning Classifier Systems. Rule Fitness and Pathology in Learning Classifier Systems. Learning Classifier Systems: A Reinforcement Learning Perspective. Learning Classifier Systems with Convergence and Generalization.-
• Section 3 - Problem Characterization. On the Classification of Maze Problems. What Makes a Problem Hard?
• (source: Nielsen Book Data)

This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
(source: Nielsen Book Data)

• Table of Contents Introduction to Reinforcement Learning Getting started with OpenAI and Tensorflow Markov Decision process and Dynamic Programming Gaming with Monte Carlo Tree Search Temporal Difference Learning Multi-Armed Bandit Problem Deep Learning Fundamentals Deep Learning and Reinforcement Playing Doom With Deep Recurrent Q Network Asynchronous Advantage Actor Critic Network Policy Gradients and Optimization Capstone Project - Car Racing using DQN Current Research and Next Steps.
• (source: Nielsen Book Data)

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art architectures along with math Book DescriptionReinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. What you will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and Tensorflow Understand the Markov Decision Process, Bellman's optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agents using the DRQN algorithm to play the Doom game Teach agents to play the Lunar Lander game using DDPG Train an agent to win a car racing game using dueling DQN Who this book is forIf you're a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
(source: Nielsen Book Data)

"Reinforcement Learning in Motion introduces you to the exciting world of machine systems that learn from their environments! Developer, data scientist, and expert instructor Phil Tabor guides you from the basics all the way to programming your own constantly-learning AI agents. In this course, he'll break down key concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents. As you learn, you'll master the core algorithms and get to grips with tools like Open AI Gym, numpy, and Matplotlib. Reinforcement systems learn by doing, and so will you in this hands-on course! You'll build and train a variety of algorithms as you go, each with a specific purpose in mind. The rich and interesting examples include simulations that train a robot to escape a maze, help a mountain car get up a steep hill, and balance a pole on a sliding cart. You'll even teach your agents how to navigate Windy Gridworld, a standard exercise for finding the optimal path even with special conditions!"--Resource description page

• Author Biographies xi List of Figures xiii List of Tables xvii Preface xix Part I Human-robot Interaction Control 1
• 1 Introduction 3 1.1 Human-Robot Interaction Control 3 1.2 Reinforcement Learning for Control 6 1.3 Structure of the Book 7 References 10
• 2 Environment Model of Human-Robot Interaction 17 2.1 Impedance and Admittance 17 2.2 Impedance Model for Human-Robot Interaction 21 2.3 Identification of Human-Robot Interaction Model 24 2.4 Conclusions 30 References 30
• 3 Model Based Human-Robot Interaction Control 33 3.1 Task Space Impedance/Admittance Control 33 3.2 Joint Space Impedance Control 36 3.3 Accuracy and Robustness 37 3.4 Simulations 39 3.5 Conclusions 42 References 44
• 4 Model Free Human-Robot Interaction Control 45 4.1 Task-Space Control Using Joint-Space Dynamics 45 4.2 Task-Space Control Using Task-Space Dynamics 52 4.3 Joint Space Control 53 4.4 Simulations 54 4.5 Experiments 55 4.6 Conclusions 68 References 71
• 5 Human-in-the-loop Control Using Euler Angles 73 5.1 Introduction 73 5.2 Joint-Space Control 74 5.3 Task-Space Control 79 5.4 Experiments 83 5.5 Conclusions 92 References 94 Part II Reinforcement Learning for Robot Interaction Control 97
• 6 Reinforcement Learning for Robot Position/Force Control 99 6.1 Introduction 99 6.2 Position/Force Control Using an Impedance Model 100 6.3 Reinforcement Learning Based Position/Force Control 103 6.4 Simulations and Experiments 110 6.5 Conclusions 117 References 117
• 7 Continuous-Time Reinforcement Learning for Force Control 119 7.1 Introduction 119 7.2 K-means Clustering for Reinforcement Learning 120 7.3 Position/Force Control Using Reinforcement Learning 124 7.4 Experiments 130 7.5 Conclusions 136 References 136
• 8 Robot Control in Worst-Case Uncertainty Using Reinforcement Learning 139 8.1 Introduction 139 8.2 Robust Control Using Discrete-Time Reinforcement Learning 141 8.3 Double Q-Learning with k-Nearest Neighbors 144 8.4 Robust Control Using Continuous-Time Reinforcement Learning 150 8.5 Simulations and Experiments: Discrete-Time Case 154 8.6 Simulations and Experiments: Continuous-Time Case 161 8.7 Conclusions 170 References 170
• 9 Redundant Robots Control Using Multi-Agent Reinforcement Learning 173 9.1 Introduction 173 9.2 Redundant Robot Control 175 9.3 Multi-Agent Reinforcement Learning for Redundant Robot Control 179 9.4 Simulations and experiments 183 9.5 Conclusions 187 References 189
• 10 Robot H2 Neural Control Using Reinforcement Learning 193 10.1 Introduction 193 10.2 H2 Neural Control Using Discrete-Time Reinforcement Learning 194 10.3 H2 Neural Control in Continuous Time 207 10.4 Examples 219 10.5 Conclusion 229 References 229
• 11 Conclusions 233 A Robot Kinematics and Dynamics 235 A.1 Kinematics 235 A.2 Dynamics 237 A.3 Examples 240 References 246 B Reinforcement Learning for Control 247 B.1 Markov decision processes 247 B.2 Value functions 248 B.3 Iterations 250 B.4 TD learning 251 Reference 258 Index 259.
• (source: Nielsen Book Data)

A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning. .
(source: Nielsen Book Data)

• 1. Introduction
• Part I. Fundamentals Without Noise: 2. Control crash course
• 3. Optimal control
• 4. ODE methods for algorithm design
• 5. Value function approximations
• Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains
• 7. Stochastic control
• 8. Stochastic approximation
• 9. Temporal difference methods
• 10. Setting the stage, return of the actors
• A. Mathematical background
• B. Markov decision processes
• C. Partial observations and belief states
• References
• Glossary of Symbols and Acronyms
• Index.
• (source: Nielsen Book Data)

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.
(source: Nielsen Book Data)

• Table of Contents Introduction to Intelligent Agents and Learning Environments Reinforcement Learning and Deep Reinforcement Learning Getting Started with OpenAI Gym and Deep Reinforcement Learning Exploring the Gym and its Features Implementing your First Learning Agent - Solving the Mountain Car problem Implementing an Intelligent Agent for Optimal Control using Deep Q-Learning Creating Custom OpenAI Gym Environments - Carla Driving Simulator Implementing an Intelligent & Autonomous Car Driving Agent using Deep Actor-Critic Algorithm Exploring the Learning Environment Landscape - Roboschool, Gym-Retro, StarCraft-II, DeepMindLab Exploring the Learning Algorithm Landscape - DDPG (Actor-Critic), PPO (Policy-Gradient), Rainbow (Value-Based).
• (source: Nielsen Book Data)

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator Key Features Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks Implement agents to solve simple to complex AI problems Study learning environments and discover how to create your own Book DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level. What you will learn Explore intelligent agents and learning environments Understand the basics of RL and deep RL Get started with OpenAI Gym and PyTorch for deep reinforcement learning Discover deep Q learning agents to solve discrete optimal control tasks Create custom learning environments for real-world problems Apply a deep actor-critic agent to drive a car autonomously in CARLA Use the latest learning environments and algorithms to upgrade your intelligent agent development skills Who this book is forIf you're a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
(source: Nielsen Book Data)

Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It's ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units.
(source: Nielsen Book Data)

Detailed summary in vernacular field.
從線性迴歸到強化學習 "對想要進入深度學習這個令人興奮的領域的機器 學習從業者來說，這是一本很棒的書。由於本書 涵蓋廣泛的主題，當你想要進一步提升技術時， 也會將它當成參考書來重新閱讀。" --Marvin Bertin Freenome機器學習研究工程師 TensorFlow是革命性的Google深度學習程式庫，本書將 教你如何用它來解決具挑戰性的機器學習問題。 只要你具備一些基本線性代數與微積分的背景知 識，就可以在這本實用的書籍學到如何設計能夠 檢查圖像物體、瞭解文字以及預測潛在藥物特性 的系統，瞭解機器學習的基礎知識。 透過實際的案例傳授觀念，協助你從根本開始建 立深厚的深度學習基礎知識。本書非常適合具備 軟體系統設計經驗的實務開發者，或已熟悉腳本 語言但不知道如何設計學習演算法的專家。 ‧學習TensorFlow的基本知識，包括如何執行基本的 計算 ‧藉由建立簡單的學習系統瞭解相關數學基礎 ‧深入瞭解已被上千種app使用的全連結深度網路 ‧藉由超參數優化將原型轉換成高品質的模型 ‧用摺積神經網路處理圖像 ‧用遞迴神經網路處理神經語言資料集 ‧使用強化學習玩遊戲，例如井字遊戲 ‧用GPU與張量處理單元等硬體訓練深度網路.