ISBN: 9798262119929 - foundations of multi-agent reinforcement learning: essential principles for researchers and developers: 1 (6 resultados)

Taschenbuch. Condición: Neu. Neuware.

Paperback. Condición: new. Paperback. Are you fascinated by artificial intelligence, but worried that complex jargon and advanced math will hold you back? You're not alone-and you don't need any prior technical experience to start your journey with Multi-Agent Reinforcement Learning (MARL).Unlock the World of Collaborative AI-Step by StepFoundations of Multi-Agent Reinforcement Learning is your friendly, confidence-building companion for understanding the fast-growing field where teams of intelligent agents learn, adapt, and collaborate to solve real-world problems. Whether you're a curious newcomer, a student, or an aspiring developer, this book guides you from zero experience to real hands-on skill-one practical step at a time.Inside, you'll discover: A beginner-friendly, jargon-free introduction to reinforcement learning and agent-based systems-explained in plain English, with personal insights and stories to keep you motivated.Real-world examples and coding exercises that let you "learn by doing"-including hands-on Python projects using the popular PettingZoo toolkit, so you can build your own smart agents from scratch.Clear explanations of key concepts such as cooperation, competition, communication, and emergent behaviors-brought to life with visualizations and practical scenarios from robotics, gaming, smart grids, and more.A supportive, encouraging approach that normalizes mistakes, celebrates small wins, and helps you overcome common challenges-so you never feel lost or alone.Step-by-step walkthroughs and troubleshooting tips for everything from setting up your environment to evaluating agent performance and interpreting results.Key takeaways you'll gain from this book: Build a strong foundation in multi-agent reinforcement learning-even if you're starting as a complete beginner.Understand the difference between single-agent and multi-agent systems, and why MARL is the key to solving complex, dynamic problems.Gain practical coding skills and experiment with real MARL environments using Python, with easy-to-follow examples and reusable templates.Learn how to benchmark, debug, and improve your agents for real-world impact.Join a supportive, fast-growing community of AI learners and developers.Break through the intimidation barrier-your journey into collaborative artificial intelligence starts here. With a warm, step-by-step teaching style and encouragement at every stage, this book is your perfect guide to mastering multi-agent reinforcement learning.Ready to dive in? Start building your skills, unlock new possibilities, and join the next generation of AI innovators-one small step at a time. Grab your copy today and let's learn together! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Are you fascinated by artificial intelligence, but worried that complex jargon and advanced math will hold you back? You're not alone-and you don't need any prior technical experience to start your journey with Multi-Agent Reinforcement Learning (MARL).Unlock the World of Collaborative AI-Step by StepFoundations of Multi-Agent Reinforcement Learning is your friendly, confidence-building companion for understanding the fast-growing field where teams of intelligent agents learn, adapt, and collaborate to solve real-world problems. Whether you're a curious newcomer, a student, or an aspiring developer, this book guides you from zero experience to real hands-on skill-one practical step at a time.Inside, you'll discover: A beginner-friendly, jargon-free introduction to reinforcement learning and agent-based systems-explained in plain English, with personal insights and stories to keep you motivated.Real-world examples and coding exercises that let you "learn by doing"-including hands-on Python projects using the popular PettingZoo toolkit, so you can build your own smart agents from scratch.Clear explanations of key concepts such as cooperation, competition, communication, and emergent behaviors-brought to life with visualizations and practical scenarios from robotics, gaming, smart grids, and more.A supportive, encouraging approach that normalizes mistakes, celebrates small wins, and helps you overcome common challenges-so you never feel lost or alone.Step-by-step walkthroughs and troubleshooting tips for everything from setting up your environment to evaluating agent performance and interpreting results.Key takeaways you'll gain from this book: Build a strong foundation in multi-agent reinforcement learning-even if you're starting as a complete beginner.Understand the difference between single-agent and multi-agent systems, and why MARL is the key to solving complex, dynamic problems.Gain practical coding skills and experiment with real MARL environments using Python, with easy-to-follow examples and reusable templates.Learn how to benchmark, debug, and improve your agents for real-world impact.Join a supportive, fast-growing community of AI learners and developers.Break through the intimidation barrier-your journey into collaborative artificial intelligence starts here. With a warm, step-by-step teaching style and encouragement at every stage, this book is your perfect guide to mastering multi-agent reinforcement learning.Ready to dive in? Start building your skills, unlock new possibilities, and join the next generation of AI innovators-one small step at a time. Grab your copy today and let's learn together! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…