Cargando…
On the complexity of computing Markov perfect equilibrium in general-sum stochastic games
Similar to the role of Markov decision processes in reinforcement learning, Markov games (also called stochastic games) lay down the foundation for the study of multi-agent reinforcement learning and sequential agent interactions. We introduce approximate Markov perfect equilibrium as a solution to...
Autores principales: | Deng, Xiaotie, Li, Ningyuan, Mguni, David, Wang, Jun, Yang, Yaodong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843164/ https://www.ncbi.nlm.nih.gov/pubmed/36684520 http://dx.doi.org/10.1093/nsr/nwac256 |
Ejemplares similares
-
Corrigendum to On the complexity of computing Markov perfect equilibrium in general-sum stochastic games
por: Deng, Xiaotie, et al.
Publicado: (2023) -
The generalized Markov measure as an equilibrium state
por: Werner, I
Publicado: (2005) -
Quantal response equilibrium for the Prisoner’s Dilemma game in Markov strategies
por: Kozitsina, T. S., et al.
Publicado: (2022) -
Non-cooperative stochastic differential game theory of generalized Markov jump linear systems
por: Zhang, Cheng-ke, et al.
Publicado: (2017) -
Zero-sum discrete-time Markov games with unknown disturbance distribution: discounted and average criteria
por: Minjárez-Sosa, J Adolfo
Publicado: (2020)