1  20
Next
 Cham : Springer, [2021]
 Description
 Book — 1 online resource : illustrations (chiefly color)
 Summary

 The Cognitive Dialogue: A New Architecture for Perception and Cognition. RooftopAware 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 LinkFlooding 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 Continuoustime 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. LongTerm Impacts of Fair Machine Learning. Learningbased Model Reduction for Partial Differential Equations with Applications to ThermoFluid Models' Identification, State Estimation, and Stabilization. CESMA: Centralized Expert Supervises MultiAgents, for Decentralization. A Unified Framework for Reinforcement Learning and Sequential Decision Analytics. Trading Utility and Uncertainty: Applying the Value of Information to Resolve the ExplorationExploitation Dilemma in Reinforcement Learning. MultiAgent 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, DecisionMaking, 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)
(source: Nielsen Book Data)
 Taylor, Matthew E.
 Berlin : Springer, ©2009.
 Description
 Book — 1 online resource (xii, 229 pages) : illustrations (some color) Digital: text file.PDF.
 Summary

 Introduction. Reinforcement Learning Background. RelatedWork. Empirical Domains. Value Function Transfer via InterTask Mappings. Extending Transfer via InterTask Mappings. Transfer between Different Reinforcement Learning Methods. Learning InterTask Mappings. Conclusion and Future work.
 (source: Nielsen Book Data)
 Liu, Teng (Ph.D. in automotive engineering), author.
 Cham, Switzerland : Springer, [2019]
 Description
 Book — 1 online resource
 Summary

 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)
(source: Nielsen Book Data)
 Cham, Switzerland : Springer, [2021]
 Description
 Book — 1 online resource (viii, 206 pages) : illustrations (some color) Digital: text file.PDF.
 Summary

 Prediction Error and ActorCritic Hypotheses in the Brain. Reviewing onpolicy / offpolicy 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. InformationLossBounded Policy Optimization. Persistent Homology for Dimensionality Reduction. Modelfree Deep Reinforcement Learning  Algorithms and Applications. Actor vs Critic. Bring Color to Deep QNetworks. Distributed Methods for Reinforcement Learning. ModelBased Reinforcement Learning. Challenges of Model Predictive Control in a Black Box Environment. Control as Inference?
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Zhao, Qing (Ph.D. in electrical engineering), author.
 Cham, Switzerland : Springer, [2020]
 Description
 Book — 1 online resource (xviii, 147 pages) : illustrations.
 Summary

 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)
(source: Nielsen Book Data)
 Ciaburro, Giuseppe, author.
 Birmingham, UK : Packt Publishing Ltd., 2018.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 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)
(source: Nielsen Book Data)
 Paper, David.
 [United States] : Apress, 2021.
 Description
 Book — 1 online resource
 Summary

 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)
(source: Nielsen Book Data)
 Sugiyama, Masashi, 1974 author.
 Boca Raton, FL : CRC Press, [2015]
 Description
 Book — 1 online resource (xiii, 189 pages) : illustrations
 Summary

 Introduction to Reinforcement Learning. ModelFree 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. ModelFree Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by ExpectationMaximization. PolicyPrior Search. ModelBased 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
 ModelFree Policy Iteration
 ModelFree Policy Search
 ModelBased Reinforcement Learning MODELFREE POLICY ITERATION Policy Iteration with Value Function Approximation Value Functions
 State Value Functions
 StateAction Value Functions LeastSquares Policy Iteration
 ImmediateReward Regression
 Algorithm
 Regularization
 Model Selection Remarks Basis Design for Value Function Approximation Gaussian Kernels on Graphs
 MDPInduced Graph
 Ordinary Gaussian Kernels
 Geodesic Gaussian Kernels
 Extension to Continuous State Spaces Illustration
 Setup
 Geodesic Gaussian Kernels
 Ordinary Gaussian Kernels
 GraphLaplacian Eigenbases
 Diffusion Wavelets Numerical Examples
 RobotArm Control
 RobotAgent Navigation Remarks Sample Reuse in Policy Iteration Formulation OffPolicy Value Function Approximation
 Episodic Importance Weighting
 PerDecision Importance Weighting
 Adaptive PerDecision Importance Weighting
 Illustration Automatic Selection of Flattening Parameter
 ImportanceWeighted CrossValidation
 Illustration SampleReuse 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
 SampleReuse 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
 DeadzoneLinear Loss
 Chebyshev Approximation
 Conditional ValueAtRisk Remarks MODELFREE 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 ExpectationMaximization ExpectationMaximization Approach Sample Reuse
 Episodic Importance Weighting
 PerDecision Importance Weight
 Adaptive PerDecision Importance Weighting
 Automatic Selection of Flattening Parameter
 RewardWeighted Regression with Sample Reuse Numerical Examples Remarks PolicyPrior Search Formulation Policy Gradients with ParameterBased Exploration
 PolicyPrior Gradient Ascent
 Baseline Subtraction for Variance Reduction
 Variance Analysis of Gradient Estimators
 Numerical Examples Sample Reuse in PolicyPrior Search
 Importance Weighting
 Variance Reduction by Baseline Subtraction
 Numerical Examples Remarks MODELBASED REINFORCEMENT LEARNING Transition Model Estimation Conditional Density Estimation
 RegressionBased Approach
 QNeighbor Kernel Density Estimation
 LeastSquares Conditional Density Estimation ModelBased Reinforcement Learning Numerical Examples
 Continuous Chain Walk
 Humanoid Robot Control Remarks Dimensionality Reduction for Transition Model Estimation Sufficient Dimensionality Reduction SquaredLoss Conditional Entropy
 Conditional Independence
 Dimensionality Reduction with SCE
 Relation to SquaredLoss Mutual Information Numerical Examples
 Artificial and Benchmark Datasets
 Humanoid Robot Remarks References Index.
 (source: Nielsen Book Data)
(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 uptodate 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 modelbased and modelfree 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 thoughtprovoking 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 stateoftheart results, including dimensionality reduction in RL and risksensitive 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 graduatelevel students in computer science and applied statistics programs, as well as researchers and engineers in related fields.
(source: Nielsen Book Data)
 Hua, Changsheng.
 Wiesbaden : Springer Vieweg, 2021.
 Description
 Book — 1 online resource (139 pages)
 Summary

 Introduction. The basics of feedback control systems. Reinforcement learning and feedback control. Qlearning aided performance optimization of deterministic systems. NAC aided performance optimization of stochastic systems. Conclusion and future work.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Nandy, Abhishek, author.
 [Berkeley, CA] : Apress, [2018]
 Description
 Book — 1 online resource Digital: text file.PDF.
 Summary

 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 : 60Subtopics1 . 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)
(source: Nielsen Book Data)
 Sanghi, Nimish.
 [Place of publication not identified] : Apress, 2021.
 Description
 Book — 1 online resource
 Summary

 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 mindmap4. 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 qlearning
 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 QLearning 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 qnetworks (DQN)2. Issues in Naive DQN 3. Introduce experience replay and target networks4. Double qlearning (DDQN)5. Duelling DQN6. Categorical 51atom DQN (C51)7. Quantile regression DQN (QRDQN)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 handson exercises1. Policy gradient approach and its advantages2. The policy gradient theorem3. REINFORCE algorithm4. REINFORCE with baseline5. Actorcritic methods6. Advantage actor critic (A2C/A3C)7. Proximal policy optimization (PPO)8. Trust region policy optimization (TRPO)
 Chapter 8: Combining Policy Gradients and QLearning 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 qlearning2. 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)
(source: Nielsen Book Data)
 Beysolow, Taweh.
 [Berkely, California] : Apress, [2019]
 Description
 Book — 1 online resource
 Summary

 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)
(source: Nielsen Book Data)
13. Foundations of learning classifier systems [2005]
 Berlin : SpringerVerlag, ©2005.
 Description
 Book — 1 online resource (vi, 336 pages) : illustrations Digital: text file.PDF.
 Summary

 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 ContinuousValued 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)
(source: Nielsen Book Data)
 Ravichandiran, Sudharsan, author.
 Birmingham, UK : Packt Publishing, 2018.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 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 MultiArmed 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)
(source: Nielsen Book Data)
15. Reinforcement learning in motion [2019]
 Tabor, Phil, speaker.
 [Place of publication not identified] : Manning Publications, 2019.
 Description
 Video — 1 online resource (1 streaming video file (5 hr., 56 min., 42 sec.))
 Summary

"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 constantlylearning 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 handson 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
 Yu, Wen (Robotics engineer), author.
 Hoboken, New Jersey : WileyIEEE Press, [2022]
 Description
 Book — 1 online resource
 Summary

 Author Biographies xi List of Figures xiii List of Tables xvii Preface xix Part I Humanrobot Interaction Control 1
 1 Introduction 3 1.1 HumanRobot Interaction Control 3 1.2 Reinforcement Learning for Control 6 1.3 Structure of the Book 7 References 10
 2 Environment Model of HumanRobot Interaction 17 2.1 Impedance and Admittance 17 2.2 Impedance Model for HumanRobot Interaction 21 2.3 Identification of HumanRobot Interaction Model 24 2.4 Conclusions 30 References 30
 3 Model Based HumanRobot 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 HumanRobot Interaction Control 45 4.1 TaskSpace Control Using JointSpace Dynamics 45 4.2 TaskSpace Control Using TaskSpace Dynamics 52 4.3 Joint Space Control 53 4.4 Simulations 54 4.5 Experiments 55 4.6 Conclusions 68 References 71
 5 Humanintheloop Control Using Euler Angles 73 5.1 Introduction 73 5.2 JointSpace Control 74 5.3 TaskSpace 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 ContinuousTime Reinforcement Learning for Force Control 119 7.1 Introduction 119 7.2 Kmeans 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 WorstCase Uncertainty Using Reinforcement Learning 139 8.1 Introduction 139 8.2 Robust Control Using DiscreteTime Reinforcement Learning 141 8.3 Double QLearning with kNearest Neighbors 144 8.4 Robust Control Using ContinuousTime Reinforcement Learning 150 8.5 Simulations and Experiments: DiscreteTime Case 154 8.6 Simulations and Experiments: ContinuousTime Case 161 8.7 Conclusions 170 References 170
 9 Redundant Robots Control Using MultiAgent Reinforcement Learning 173 9.1 Introduction 173 9.2 Redundant Robot Control 175 9.3 MultiAgent 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 DiscreteTime 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)
(source: Nielsen Book Data)
17. Control systems and reinforcement learning [2022]
 Meyn, S. P. (Sean P.), author.
 Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2022.
 Description
 Book — 1 online resource (xv, 435 pages) : illustrations
 Summary

 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)
(source: Nielsen Book Data)
 Palanisamy, Praveen, author.
 Birmingham, UK : Packt Publishing, 2018.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 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 QLearning Creating Custom OpenAI Gym Environments  Carla Driving Simulator Implementing an Intelligent & Autonomous Car Driving Agent using Deep ActorCritic Algorithm Exploring the Learning Environment Landscape  Roboschool, GymRetro, StarCraftII, DeepMindLab Exploring the Learning Algorithm Landscape  DDPG (ActorCritic), PPO (PolicyGradient), Rainbow (ValueBased).
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Ramsundar, Bharath, author.
 First edition.  Sebastopol, CA : O'Reilly Media, [2018]
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

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 machinelearning 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 highquality 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 tictactoe Train deep networks with hardware including GPUs and tensor processing units.
(source: Nielsen Book Data)
 初探深度學習 : 使用 TensorFlow = TensorFlow for deep learning : from linear regression to reinforcement learning
 TensorFlow for deep learning. Chinese
 Ramsundar, Bharath, author.
 [First edition].  [Place of publication not identified] : GoTop Information, Inc., [2018]
 Description
 Book — 1 online resource (256 pages) : illustrations
 Summary

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.
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