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Deep Multi-Agent Reinforcement Learning: Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Agent Decision Making - Couverture souple
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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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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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798185265246
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Deep Multi-Agent Reinforcement Learning (Paperback)
impression à la demandeVendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : 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 multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798185265246
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Deep Multi-Agent Reinforcement Learning
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798185265246
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Deep Multi-Agent Reinforcement Learning : Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Agent Decision Making
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : 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. N° de réf. du vendeur 9798185265246
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EUR 46,55
Expédition à EUR 35
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
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Deep Multi-Agent Reinforcement Learning (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : 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. N° de réf. du vendeur 9798185265246
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EUR 38,87
Expédition à EUR 43,65
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
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