New method for learning equilibria in unbounded stochastic games

Researchers have developed a new procedure for learning equilibria in unbounded stochastic games with Gaussian transitions. This method utilizes a single data trajectory to update Gaussian-process models of the system's drift and to solve successively refined finite games. The procedure is proven to ensure stability of equilibrium selection and uniform KL accuracy of Gaussian transition models, leading to convergence of values and approximate Nash behavior in the original game. AI

IMPACT Introduces a novel statistical learning technique for complex game theory scenarios.

RANK_REASON This is a research paper published on arXiv detailing a new statistical learning procedure. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Euclidean
• Gaussian process
• Gaussian transitions
• Jonathan Mavroforas

• paper
• other

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