Deep Multi-Agent Reinforcement Learning: Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Ag

Name: Deep Multi-Agent Reinforcement Learning: Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Ag
Brand: Independently Published
SKU: 9798185265246
Price: 2983.0 INR
Availability: InStock

by Julian Stonemere

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ISBN13: 9798185265246
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: July 2, 2026
Pages: 260
Original Price: GBP 26.36
Language: English
Edition: N/A
Item Weight: 459 grams
BISAC Subject(s): Data Science / Neural Networks

About The Book

Your first multi-agent RL project will teach you a hard truth: everything you know about single-agent training breaks the moment a second learner enters the room.
Non-stationarity sets in. Rewards stop meaning what you think they mean. And the fixes that worked for a single policy quietly make things worse.
This is the book for engineers and researchers who already know single-agent RL and are ready for what comes next - written by a practitioner who's built coordinating robot fleets, adversarial trading agents, and cooperating LLM agent teams, and who still remembers exactly where it went wrong the first time.
Inside, you'll learn:

Why non-stationarity is the real enemy of MARL - and how to design around it
How to formulate state, observation, action, and reward before you write training code (the highest-leverage decision in any MARL project)
Cooperative methods: value decomposition (VDN, QMIX), credit assignment, and learned communication
Competitive methods: self-play, opponent modeling, exploitability, and why average return lies to you
Scaling to dozens or hundreds of agents without training collapsing
Graph neural networks, mean-field methods, and attention-based communication architectures
Real deployment: sim-to-real transfer, robotics, swarms, and multi-agent LLM systems
Where the field is still unsolved - continual learning, human-AI teams, and multi-agent alignment

Written in first person, with real mistakes included, not just the theory that made it into the papers. Every chapter builds a working intuition, then shows you exactly how it fails in practice - so you find out in the book, not three weeks into a training run.
If you've trained a MARL system, watched it behave strangely, and wanted to know why - this book is for you.

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