Isbn: 9798185265246 - deep multi-agent reinforcement learning: algorithms, cooperation, competition, communication learning, graph neural networks, and large-scale multi-agent decision making (6 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: 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.…

Condizione: New. Print on Demand.

Paperback. Condizione: 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.…

Paperback. Condizione: 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.…