Reinforcement Learning for Finance, Ahlawat

Автор: Shengbo Eben Li
Название: Reinforcement Learning for Sequential Decision and Optimal Control
ISBN: 9811977836 ISBN-13(EAN): 9789811977831
Издательство: Springer
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Цена: 74530.00 T
Наличие на складе: Поставка под заказ.
Описание: Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman’s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on.

Автор: Farsi, Milad,
Название: Model-based reinforcement learning :
ISBN: 111980857X ISBN-13(EAN): 9781119808572
Издательство: Wiley
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Цена: 108770.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is for researchers and students in statistics, data mining, computer science, machine learning, marketing and also practitioners who implement recommender systems. It provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and state-of-the-art solutions in personalization, explore/exploit, dimension reduction and multi-objective optimization.

Автор: Lonza, Andrea
Название: Reinforcement learning algorithms with python
ISBN: 1789131111 ISBN-13(EAN): 9781789131116
Издательство: Неизвестно
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Цена: 47810.00 T
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Описание: With this book, you will understand the core concepts and techniques of reinforcement learning. You will take a look into each RL algorithm and will develop your own self-learning algorithms and models. You will optimize the algorithms for better precision, use high-speed actions and lower the risk of anomalies in your applications.

Автор: Li, Chong
Название: Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies
ISBN: 1138543535 ISBN-13(EAN): 9781138543539
Издательство: Taylor&Francis
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Цена: 84710.00 T
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Описание: This book introduces reinforcement learning, and provides novel ideas and use cases to demonstrate the benefits of using reinforcement learning for Cyber Physical Systems. Two important case studies on applying reinforcement learning to cybersecurity problems are included.

Автор: Osborne
Название: Applying Reinforcement Learning on Real-World Data with Practical Examples in Python
ISBN: 3031791665 ISBN-13(EAN): 9783031791666
Издательство: Springer
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Цена: 51230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals.

Автор: Michael C. Fu, Prashanth L. A.
Название: Risk-Sensitive Reinforcement Learning Via Policy Gradient Search
ISBN: 1638280266 ISBN-13(EAN): 9781638280262
Издательство: Mare Nostrum (Eurospan)
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Цена: 96090.00 T
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Описание: Reinforcement learning (RL) is one of the foundational pillars of artificial intelligence and machine learning. An important consideration in any optimization or control problem is the notion of risk, but its incorporation into RL has been a recent development. This monograph surveys research on risk-sensitive RL that uses policy gradient search.

Автор: Liu Yuxi (Hayden)
Название: PyTorch 1.0 Reinforcement Learning Cookbook
ISBN: 1838551964 ISBN-13(EAN): 9781838551964
Издательство: Неизвестно
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Цена: 53940.00 T
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Описание: This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.

Автор: Vamvoudakis Kyriakos G., Wan Yan, Lewis Frank L.
Название: Handbook of Reinforcement Learning and Control
ISBN: 3030609898 ISBN-13(EAN): 9783030609894
Издательство: Springer
Цена: 214280.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: B?r
Название: Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
ISBN: 3658391782 ISBN-13(EAN): 9783658391782
Издательство: Springer
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Цена: 46570.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.

Автор: Rao, Ashwin (stanford University, Usa) Jelvis, Tikhon
Название: Foundations of reinforcement learning with applications in finance
ISBN: 1032124121 ISBN-13(EAN): 9781032124124
Издательство: Taylor&Francis
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Цена: 76550.00 T
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Описание: This book demystifies Reinforcement Learning, and makes it a practically useful tool for those studying and working in applied areas, especially finance. This book seeks to overcome that barrier, and to introduce the foundations of RL in a way that balances depth of understanding with clear, minimally technical delivery.

Автор: Sugiyama, Masashi
Название: Statistical Reinforcement Learning
ISBN: 0367575868 ISBN-13(EAN): 9780367575861
Издательство: Taylor&Francis
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Цена: 45930.00 T
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Автор: Hua, Changsheng
Название: Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
ISBN: 3658330333 ISBN-13(EAN): 9783658330330
Издательство: Springer
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Цена: 65210.00 T
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Описание: 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.