A proximal/gradient approach for computing the Nash equilibrium in controllable Markov games

DOI10.1007/s10957-021-01812-3zbMath1466.91011OpenAlexW3125956201MaRDI QIDQ2031943

Publication date: 15 June 2021

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10957-021-01812-3

zbMATH Keywords

algorithm regularization Nash equilibrium noncooperative game theory proximal gradient

Mathematics Subject Classification ID

Noncooperative games (91A10) Stochastic games, stochastic differential games (91A15) Algorithmic game theory and complexity (91A68) Equilibrium refinements (91A11)

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A Bayesian reinforcement learning approach in Markov games for computing near-optimal policies

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