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.
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Deep Multi-Agent Reinforcement Learning: Algorithms, Cooperation, Competition, Communication Learning, Graph Neural Networks, and Large-Scale Multi-Agent Decision Making
impression à la demandeVendeur : California Books, Miami, FL, Etats-Unis
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Etat : New. Print on Demand. N° de réf. du vendeur I-9798185265246
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EUR 33,31
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Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
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