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  • Libro 3 de 3: Multi-Agent Reinforcement Learning Mastery Series for Researchers and Developers

    Lauritsen, Marcus C.

    Idioma: Inglés

    Publicado por Amazon Digital Services LLC - Kdp, 2025

    ISBN 13: 9798268919318

    Librería: PBShop.store UK, Fairford, GLOS, Reino Unido

    Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

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    EUR 27,57

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    PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

  • EUR 27,04

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    Condición: New. Print on Demand.

  • Libro 3 de 3: Multi-Agent Reinforcement Learning Mastery Series for Researchers and Developers

    Marcus C. Lauritsen

    Idioma: Inglés

    Publicado por Independently Published, 2025

    ISBN 13: 9798268919318

    Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America

    Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

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    EUR 30,34

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    Paperback. Condición: new. Paperback. Unlock the Power of Multi-Agent Reinforcement Learning-No Experience Required!Do you ever look at advanced AI, robotics, or machine learning and wonder, "Could I ever understand this?" Maybe you've felt overwhelmed by complex code, technical jargon, or the sheer speed at which technology is advancing. You're not alone-and this book was written just for you.Advanced Multi-Agent Reinforcement Learning: Cutting-Edge Techniques, Coordination, and Optimization is your supportive, step-by-step guide to one of the most exciting frontiers in artificial intelligence. Designed for absolute beginners, this book gently demystifies multi-agent systems and deep reinforcement learning, showing you how teams of intelligent agents learn, adapt, and cooperate-one clear concept at a time.No math PhD, coding background, or prior AI experience needed. Every chapter breaks down key ideas with warmth and encouragement, celebrating progress and making learning a truly enjoyable journey. You'll move from simple Python code to understanding real-world robotics, smart automation, and advanced AI algorithms-all at your own pace.Inside, you'll discover: Friendly explanations of multi-agent reinforcement learning (MARL), deep RL, and distributed AI-perfect for curious beginners.Hands-on projects and real-world examples with clear, annotated Python code you can run, tweak, and learn from-no setup headaches.Practical skills for building, testing, and understanding intelligent agents in simulations, robotics, and collaborative AI.Step-by-step guidance that nurtures your confidence, normalizes mistakes, and celebrates small victories at every stage.Expert tips on troubleshooting, benchmarking, and applying your knowledge to real challenges-plus advice on where to go next.Supportive encouragement throughout, making even the toughest concepts feel manageable and fun.What makes this book unique?Written with empathy for newcomers-every term is explained, every code sample is walk-through-friendly.Focuses on real understanding, not just copying code or memorizing formulas.Covers the latest tools and frameworks (PettingZoo, RLlib), ensuring your skills are practical and up to date.Includes personal insights and stories to remind you: every AI expert started with the basics, just like you.You'll learn how to: Design intelligent agent teams using multi-agent reinforcement learning.Apply value decomposition and centralized training techniques for smarter coordination.Benchmark, evaluate, and troubleshoot your own AI experiments with confidence.See how MARL is changing robotics, automation, and the future of technology.No prior coding or AI experience required-just a willingness to explore, experiment, and enjoy the process.If you're ready to stop feeling intimidated by AI and start building real skills, this is your moment. Let Advanced Multi-Agent Reinforcement Learning be your trusted guide and companion. Start your journey today-and celebrate every step forward in the world of intelligent, collaborative technology!Take the first step now! Your future in AI starts right here, one encouraging page at a time. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Libro 3 de 3: Multi-Agent Reinforcement Learning Mastery Series for Researchers and Developers

    Marcus C. Lauritsen

    Idioma: Inglés

    Publicado por Independently Published, 2025

    ISBN 13: 9798268919318

    Librería: CitiRetail, Stevenage, Reino Unido

    Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

    Contactar al vendedor

    Impresión bajo demanda

    EUR 31,50

    Envío por EUR 43,53
    Se envía de Reino Unido a Estados Unidos de America

    Cantidad disponible: 1 disponibles

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    Paperback. Condición: new. Paperback. Unlock the Power of Multi-Agent Reinforcement Learning-No Experience Required!Do you ever look at advanced AI, robotics, or machine learning and wonder, "Could I ever understand this?" Maybe you've felt overwhelmed by complex code, technical jargon, or the sheer speed at which technology is advancing. You're not alone-and this book was written just for you.Advanced Multi-Agent Reinforcement Learning: Cutting-Edge Techniques, Coordination, and Optimization is your supportive, step-by-step guide to one of the most exciting frontiers in artificial intelligence. Designed for absolute beginners, this book gently demystifies multi-agent systems and deep reinforcement learning, showing you how teams of intelligent agents learn, adapt, and cooperate-one clear concept at a time.No math PhD, coding background, or prior AI experience needed. Every chapter breaks down key ideas with warmth and encouragement, celebrating progress and making learning a truly enjoyable journey. You'll move from simple Python code to understanding real-world robotics, smart automation, and advanced AI algorithms-all at your own pace.Inside, you'll discover: Friendly explanations of multi-agent reinforcement learning (MARL), deep RL, and distributed AI-perfect for curious beginners.Hands-on projects and real-world examples with clear, annotated Python code you can run, tweak, and learn from-no setup headaches.Practical skills for building, testing, and understanding intelligent agents in simulations, robotics, and collaborative AI.Step-by-step guidance that nurtures your confidence, normalizes mistakes, and celebrates small victories at every stage.Expert tips on troubleshooting, benchmarking, and applying your knowledge to real challenges-plus advice on where to go next.Supportive encouragement throughout, making even the toughest concepts feel manageable and fun.What makes this book unique?Written with empathy for newcomers-every term is explained, every code sample is walk-through-friendly.Focuses on real understanding, not just copying code or memorizing formulas.Covers the latest tools and frameworks (PettingZoo, RLlib), ensuring your skills are practical and up to date.Includes personal insights and stories to remind you: every AI expert started with the basics, just like you.You'll learn how to: Design intelligent agent teams using multi-agent reinforcement learning.Apply value decomposition and centralized training techniques for smarter coordination.Benchmark, evaluate, and troubleshoot your own AI experiments with confidence.See how MARL is changing robotics, automation, and the future of technology.No prior coding or AI experience required-just a willingness to explore, experiment, and enjoy the process.If you're ready to stop feeling intimidated by AI and start building real skills, this is your moment. Let Advanced Multi-Agent Reinforcement Learning be your trusted guide and companion. Start your journey today-and celebrate every step forward in the world of intelligent, collaborative technology!Take the first step now! Your future in AI starts right here, one encouraging page at a time. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.