Mastering Multi-Agent Reinforcement Learning (Paperback)
Idioma: inglés
Editorial: Independently Published, 2025
- Tapa blanda
- Nuevo
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de IberLibro desde 12 de octubre de 2005
Condición: Nuevo
EUR 32,95
Cantidad disponible: 1 disponible
Añadir al carritoDescripción del artículo del vendedor
Paperback. Mastering Multi-Agent Reinforcement Learning: From Foundations to SimulationsFeeling curious about AI but worried it's too advanced-or that multi-agent systems sound like rocket science? This book meets you right where you are. Written in warm, plain language, it starts from zero and guides you step by step through the core ideas of reinforcement learning (RL) before gently opening the door to multi-agent RL (MARL). No prior coding, math, or AI background required. You'll build confidence one small win at a time and see how agents learn to cooperate, compete, and adapt in simulated worlds.You won't just read about concepts-you'll practice them. Each chapter focuses on one clear idea, shows it in action, and then helps you reflect on what worked and what didn't. Mistakes are expected and celebrated: they're signals that you're learning. By the end, you'll understand how to design environments, shape rewards, evaluate performance, and run meaningful experiments that scale from single agents to many.What you'll learn-clearly and calmlyThe essentials of reinforcement learning: states, actions, rewards, policies, and value functions-explained for beginners.How multi-agent systems differ (and why they matter): cooperation, competition, communication, and fair evaluation.A friendly path from single-agent RL to MARL using popular open-source environments and tools.Practical skills: setting up experiments, shaping rewards, choosing algorithms, logging metrics, and comparing results.Reproducible workflows: clean project structure, sensible defaults, and checklists that prevent common pitfalls.Real-world thinking: when to prefer centralized training with decentralized execution (CTDE), how to tune hyperparameters without guesswork, and how to spot overfitting early.Why this book is differentBeginner-first voice: jargon kept to a minimum; every new term is introduced gently and used consistently.Step-by-step projects: small, complete exercises that build into larger simulations you can actually run and understand.Confidence building: reflective prompts, progress checkpoints, and simple diagnostics make your learning feel visible.Hands-on and modern: examples align with current RL/MARL practices so your skills transfer to real projects.Perfect for complete beginners, self-taught developers, students exploring AI, and anyone who wants a friendly, structured path into multi-agent reinforcement learning without feeling overwhelmed.If you've been waiting for a clear, supportive guide to RL and MARL-one that treats you like a capable beginner and turns complex ideas into doable steps-this is it. Start today, build your first simulations, and discover how intelligent agents learn together. Your AI journey begins now. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
N° de ref. del artículo 9798298480376
- Título
- Mastering Multi-Agent Reinforcement Learning (Paperback)
- Autor
- Haider Koele
- Editorial
- Independently Published
- Año de publicación
- 2025
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 13
- 9798298480376
Mastering Multi-Agent Reinforcement Learning: From Foundations to Simulations
Feeling curious about AI but worried it’s too advanced—or that multi-agent systems sound like rocket science? This book meets you right where you are. Written in warm, plain language, it starts from zero and guides you step by step through the core ideas of reinforcement learning (RL) before gently opening the door to multi-agent RL (MARL). No prior coding, math, or AI background required. You’ll build confidence one small win at a time and see how agents learn to cooperate, compete, and adapt in simulated worlds.
You won’t just read about concepts—you’ll practice them. Each chapter focuses on one clear idea, shows it in action, and then helps you reflect on what worked and what didn’t. Mistakes are expected and celebrated: they’re signals that you’re learning. By the end, you’ll understand how to design environments, shape rewards, evaluate performance, and run meaningful experiments that scale from single agents to many.
What you’ll learn—clearly and calmly
-
The essentials of reinforcement learning: states, actions, rewards, policies, and value functions—explained for beginners.
-
How multi-agent systems differ (and why they matter): cooperation, competition, communication, and fair evaluation.
-
A friendly path from single-agent RL to MARL using popular open-source environments and tools.
-
Practical skills: setting up experiments, shaping rewards, choosing algorithms, logging metrics, and comparing results.
-
Reproducible workflows: clean project structure, sensible defaults, and checklists that prevent common pitfalls.
-
Real-world thinking: when to prefer centralized training with decentralized execution (CTDE), how to tune hyperparameters without guesswork, and how to spot overfitting early.
Why this book is different
-
Beginner-first voice: jargon kept to a minimum; every new term is introduced gently and used consistently.
-
Step-by-step projects: small, complete exercises that build into larger simulations you can actually run and understand.
-
Confidence building: reflective prompts, progress checkpoints, and simple diagnostics make your learning feel visible.
-
Hands-on and modern: examples align with current RL/MARL practices so your skills transfer to real projects.
Perfect for complete beginners, self-taught developers, students exploring AI, and anyone who wants a friendly, structured path into multi-agent reinforcement learning without feeling overwhelmed.
If you’ve been waiting for a clear, supportive guide to RL and MARL—one that treats you like a capable beginner and turns complex ideas into doable steps—this is it. Start today, build your first simulations, and discover how intelligent agents learn together. Your AI journey begins now.
“Sinopsis” puede pertenecer a otra edición de este título.
Grand Eagle Retail
Bensenville, IL, Estados Unidos de America
Vendedor de IberLibro desde 12 de octubre de 2005
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