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Deep Multi-Agent Reinforcement Learning: Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Agent Decision Making - Softcover
Synopsis
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
If you've trained a MARL system, watched it behave strangely, and wanted to know why — this book is for you.
"synopsis" may belong to another edition of this title.
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Deep Multi-Agent Reinforcement Learning: Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Agent Decision Making
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Deep Multi-Agent Reinforcement Learning
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Deep Multi-Agent Reinforcement Learning
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Deep Multi-Agent Reinforcement Learning (Paperback)
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798185265246
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Deep Multi-Agent Reinforcement Learning : Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Agent Decision Making
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Taschenbuch. Condition: Neu. Neuware - 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 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. Seller Inventory # 9798185265246
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