MuZero Algorithms and Applications (eBook)

The Complete Guide for Developers and Engineers

William Smith (Autor)

eBook Download: EPUB

2025 | 1. Auflage
250 Seiten
HiTeX Press (Verlag)
978-0-00-103034-3 (ISBN)

Infos

'MuZero Algorithms and Applications'
'MuZero Algorithms and Applications' delivers a comprehensive exploration of DeepMind's MuZero, one of the most influential breakthroughs at the intersection of model-based and model-free reinforcement learning. The book begins with a thoughtful exposition of foundational concepts, situating MuZero within the broader landscape of model-based methods and meticulously analyzing the limitations of its predecessors. Readers are guided through MuZero's hallmark innovations-including its synergistic use of value, policy, and dynamics modeling, integration with Monte Carlo Tree Search, and theoretical guarantees-providing an intuitive yet rigorous understanding of the algorithm's core strengths, convergence properties, and its comparative edge over AlphaZero and classical techniques.
Moving beyond theory, the text delves into the architectural and procedural subtleties that define MuZero's practical effectiveness. Chapters dissect representation, dynamics, and prediction functions; unveil the neural network structures and training strategies essential for stability; and offer robust guidance on data handling, optimization, distributed training, and hyperparameter tuning. The book pays special attention to challenges such as partial observability, uncertainty quantification, overfitting prevention, and generalization across diverse environments. Readers benefit from expert insights on advanced algorithmic extensions-spanning stochasticity, hierarchy, meta-learning, hybrid architectures, and recent experimental innovations-making this volume indispensable for practitioners aiming to push the boundaries of reinforcement learning.
Bridging theory, practice, and real-world impact, 'MuZero Algorithms and Applications' presents a wealth of case studies spanning board games, Atari and video game benchmarks, robotics, operations research, autonomous systems, healthcare, finance, and more. The text rigorously outlines evaluation strategies, interpretability tools, reproducibility best practices, and illustrates the algorithm's performance through comparative results and ablation studies. In its concluding chapters, the book confronts current challenges, from computational bottlenecks and theoretical gaps to ethical considerations and future research directions, making it a definitive and forward-looking reference for researchers, engineers, and application-focused professionals shaping the future of intelligent sequential decision-making.

Textauszug

Chapter 1
Principles and Theory of MuZero

What happens when we let a reinforcement learning agent master abstract planning, without ever seeing the rules of its environment? This chapter pulls back the curtain on MuZero’s fascinating theoretical foundations, revealing how it elegantly bypasses the need for full environment models to achieve state-of-the-art results. By closely examining MuZero’s formulation, learning objectives, and search integration, you’ll see how it challenges conventions in RL, combines the best of model-based and model-free thinking, and reshapes our approach to intelligent sequential decision-making.

1.1 Introduction to Model-based Reinforcement Learning

Model-based reinforcement learning (MBRL) constitutes a fundamental paradigm within the broader reinforcement learning (RL) framework, distinguished primarily by its explicit construction and utilization of an internal model of the environment. Unlike model-free methods, which directly learn policies or value functions through trial-and-error interactions, model-based approaches leverage predictive models to facilitate planning and policy generation. Formally, an MBRL agent typically maintains a transition model T approximating the environment’s state dynamics, and a reward model R estimating immediate rewards, enabling the simulation of future trajectories without exhaustive real-world exploration.

At its core, the transition model T :

• Erscheint lt. Verlag: 19.8.2025
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• ISBN-10: 0-00-103034-5 / 0001030345
• ISBN-13: 978-0-00-103034-3 / 9780001030343