{"product_id":"deep-multi-agent-reinforcement-learning-algorithms-cooperation-competition-communication-learning-graph-neural-networks-and-large-scale-multi-ag-9798185265246","title":"Deep Multi-Agent Reinforcement Learning: Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Ag","description":"\u003cp\u003e • Author(s): Julian Stonemere\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Neural Networks\u003c\/p\u003e\u003cp\u003e\u003cb\u003eYour 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.\u003c\/b\u003e\u003cbr\u003eNon-stationarity sets in. Rewards stop meaning what you think they mean. And the fixes that worked for a single policy quietly make things worse.\u003cbr\u003eThis 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.\u003cbr\u003e\u003cb\u003eInside, you'll learn: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eWhy non-stationarity is the real enemy of MARL - and how to design around it\u003c\/li\u003e\n\u003cli\u003eHow to formulate state, observation, action, and reward \u003ci\u003ebefore\u003c\/i\u003e you write training code (the highest-leverage decision in any MARL project)\u003c\/li\u003e\n\u003cli\u003eCooperative methods: value decomposition (VDN, QMIX), credit assignment, and learned communication\u003c\/li\u003e\n\u003cli\u003eCompetitive methods: self-play, opponent modeling, exploitability, and why average return lies to you\u003c\/li\u003e\n\u003cli\u003eScaling to dozens or hundreds of agents without training collapsing\u003c\/li\u003e\n\u003cli\u003eGraph neural networks, mean-field methods, and attention-based communication architectures\u003c\/li\u003e\n\u003cli\u003eReal deployment: sim-to-real transfer, robotics, swarms, and multi-agent LLM systems\u003c\/li\u003e\n\u003cli\u003eWhere the field is still unsolved - continual learning, human-AI teams, and multi-agent alignment\u003c\/li\u003e\n\u003c\/ul\u003eWritten 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.\u003cbr\u003e\u003cb\u003eIf you've trained a MARL system, watched it behave strangely, and wanted to know \u003ci\u003ewhy\u003c\/i\u003e - this book is for you.\u003c\/b\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":48211718733975,"sku":"9798185265246","price":2983.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798185265246.webp?v=1788720750","url":"https:\/\/atlanticbooks.com\/products\/deep-multi-agent-reinforcement-learning-algorithms-cooperation-competition-communication-learning-graph-neural-networks-and-large-scale-multi-ag-9798185265246","provider":"Atlantic Books","version":"1.0","type":"link"}