Reinforcement Learning
Reference documents in Reinforcement Learning.
Documents in Reinforcement Learning
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This book provides a clear and simple account of the field's key ideas and algorithms of Reinforcement Learning (RL).
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The purpose of the book is to give an overview of the Reinforcement Learning (RL) methodology, with a particular focus on problems of optimal and suboptimal control, as well as discrete optimization.
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This book aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas ― especially finance, implementing models and algorithms in well-designed Python code.
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This book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic Reinforcement Learning (RL) algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout.
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Reinforcement Learning (RL) agents have demonstrated remarkable achievements in simulated environments. This tutorial offers a framework that can guide associated agent design decisions.
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Bridging the gap between introductory texts and the specialized research literature, this self-contained book with practical applications introduces the foundations of sequential decision-making and intelligent agent design, uniquely combines mathematical precision with practical insights.
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Reinforcement Learning (RL), one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment.
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The purpose of the course is to give an overview of the Reinforcement Learning (RL) methodology, particularly as it relates to problems of optimal and suboptimal decision and control, as well as discrete optimization.
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This book provides a clear and simple account of the field's key ideas and algorithms of Reinforcement Learning (RL).
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The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.
