Multi-Agent Reinforcement Learning: Foundations and Modern Approaches (1版)

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【簡介】 The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.Multi-Agent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multi-robot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the field’s foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and self-play. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are self-contained and easy to read. Technical content is explained in easy-to-understand language and illustrated with extensive examples, illuminating MARL for newcomers while offering high-level insights for more advanced readers. First textbook to introduce the foundations and applications of MARL, written by experts in the fieldIntegrates reinforcement learning, deep learning, and game theoryPractical focus covers considerations for running experiments and describes environments for testing MARL algorithmsExplains complex concepts in clear and simple languageClassroom-tested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics Resources include code and slides

Modern Take on Physics, a - Volume 2: Classical and Quantum Field Theory (1版)

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【簡介】 These two volumes cover the classical topics typically taught in undergraduate physics courses and beyond. However, we teach classical physics using a very different language from traditional books, influenced by the language of modern physics, in a move to encourage a positive approach towards the latter subject. Based around the unifying concept of energy, we take a distinct approach to the organisation of material, presenting physics in a cohesive manner without separating topics such as mechanics, kinematics, thermodynamics, electricity, and magnetism. Instead, physics is revealed gradually, emphasising a holistic approach to science. Despite this peculiarity, we have maintained the conventional separation between Physics I and II to assist teachers in smoothly integrating these books into their courses.These innovative volumes present valuable new perspectives. Although their approach is bold, they will certainly prompt interesting reflections on traditional teachings. A significant portion of this second volume is committed to modern physics, including an introduction to the standard model of fundamental interactions. The old quantum mechanics is largely excluded in favour of quantum field theory, which we present in a manner accessible to undergraduate students.

Lectures on the Theory and Application of Modern Finance with R and ChatGPT (1版)

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【簡介】 These lecture notes are thought for Master courses in Finance, Fintech and Quantitative Finance programmes. We fully subscribe to the philosophy that post-graduate students should be offered courses that are really at the cutting edge of the technologies and advances that are disrupting the financial industry and delve deep into topics such as A.I., machine learning, and their importance for Asset Management.In these notes, the illustration of the theory of Finance is paired with practical applications to real-life asset allocation problems. A hands-on approach is proposed to construct and manipulate databases to build portfolios, assess their performance and manage their risk. The course begins with a section on the fundamentals on individual choice to market valuation, covering the traditional Markowitz mean-variance approach, market-based asset pricing and Arbitrage-based pricing theory.Empirical modelling in finance is then introduced by illustrating its working and its historical evolution. The translation of financial theory into action on data is driven by building predictive models for asset prices and returns. Basic models are explored, and programming emerges as an essential prerequisite for data manipulation. Readers can acquaint themselves with the statistical software R and exhibit the application of theoretical concepts to financial data, illustrated by sample programs, exercises, and corresponding solutions.