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Sample Efficient Multiagent Learning in the Presence of Markovian Agents [electronic resource] / by Doran Chakraborty.

By: Chakraborty, Doran [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 523Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XVIII, 147 p. 31 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319026060.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Background -- Learn or Exploit in Adversary Induced Markov Decision Processes -- Convergence, Targeted Optimality and Safety in Multiagent Learning -- Maximizing -- Targeted Modeling of Markovian agents -- Structure Learning in Factored MDPs -- Related Work -- Conclusion and Future Work.
In: Springer eBooksSummary: The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties.
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Introduction -- Background -- Learn or Exploit in Adversary Induced Markov Decision Processes -- Convergence, Targeted Optimality and Safety in Multiagent Learning -- Maximizing -- Targeted Modeling of Markovian agents -- Structure Learning in Factored MDPs -- Related Work -- Conclusion and Future Work.

The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties.

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