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{ "item_title" : "Deep Multi-Agent Reinforcement Learning", "item_author" : [" Julian Stonemere "], "item_description" : "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 itHow 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 communicationCompetitive methods: self-play, opponent modeling, exploitability, and why average return lies to youScaling to dozens or hundreds of agents without training collapsingGraph neural networks, mean-field methods, and attention-based communication architecturesReal deployment: sim-to-real transfer, robotics, swarms, and multi-agent LLM systemsWhere the field is still unsolved - continual learning, human-AI teams, and multi-agent alignmentWritten 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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/818/526/9798185265246_b.jpg", "price_data" : { "retail_price" : "35.00", "online_price" : "35.00", "our_price" : "35.00", "club_price" : "35.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Deep Multi-Agent Reinforcement Learning|Julian Stonemere

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

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9798185265246
  • ISBN-10: 9798185265246
  • Publisher: Independently Published
  • Publish Date: July 2026
  • Dimensions: 10 x 7 x 0.55 inches
  • Shipping Weight: 1.01 pounds
  • Page Count: 260

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