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Descripción
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| We extend earlier works on continuous potential games to the most general case: stochastic time varying environment, stochastic rewards, non-reduced form and constrained state-action sets. We provide conditions for a Markov Nash equilibrium (MNE) of the game to be equivalent to the solution of a single control problem. Then, we address the problem of learning this MNE when the reward and state transition models are unknown. We follow a reinforcement learning approach and extend previous algorithms for working with constrained state-action subsets of real vector spaces. As an application example, we simulate a network flow optimization model, in which the relays have batteries that deplete with a random factor. The results obtained with the proposed framework are close to optimal. | |
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Internacional
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Si |
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Nombre congreso
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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
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Tipo de participación
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970 |
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Lugar del congreso
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Shanghai, China |
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Revisores
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Si |
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ISBN o ISSN
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2379-190X |
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DOI
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10.1109/ICASSP.2016.7472542 |
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Fecha inicio congreso
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20/03/2016 |
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Fecha fin congreso
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25/05/2017 |
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Desde la página
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1 |
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Hasta la página
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5 |
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Título de las actas
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Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on |