124 research outputs found
Relative Value Iteration for Stochastic Differential Games
We study zero-sum stochastic differential games with player dynamics governed
by a nondegenerate controlled diffusion process. Under the assumption of
uniform stability, we establish the existence of a solution to the Isaac's
equation for the ergodic game and characterize the optimal stationary
strategies. The data is not assumed to be bounded, nor do we assume geometric
ergodicity. Thus our results extend previous work in the literature. We also
study a relative value iteration scheme that takes the form of a parabolic
Isaac's equation. Under the hypothesis of geometric ergodicity we show that the
relative value iteration converges to the elliptic Isaac's equation as time
goes to infinity. We use these results to establish convergence of the relative
value iteration for risk-sensitive control problems under an asymptotic
flatness assumption
Comparison of Random Walk Based Techniques for Estimating Network Averages
International audienceFunction estimation on Online Social Networks (OSN) is an important field of study in complex network analysis. An efficient way to do function estimation on large networks is to use random walks. We can then defer to the extensive theory of Markov chains to do error analysis of these estimators. In this work we compare two existing techniques, Metropolis-Hastings MCMC and Respondent-Driven Sampling, that use random walks to do function estimation and compare them with a new reinforcement learning based technique. We provide both theoretical and empirical analyses for the estimators we consider
Geometrical Insights for Implicit Generative Modeling
Learning algorithms for implicit generative models can optimize a variety of
criteria that measure how the data distribution differs from the implicit model
distribution, including the Wasserstein distance, the Energy distance, and the
Maximum Mean Discrepancy criterion. A careful look at the geometries induced by
these distances on the space of probability measures reveals interesting
differences. In particular, we can establish surprising approximate global
convergence guarantees for the -Wasserstein distance,even when the
parametric generator has a nonconvex parametrization.Comment: this version fixes a typo in a definitio
Compactness of the space of non-randomized policies in countable-state sequential decision processes
N—Person Stochastic Games: Extensions of the Finite State Space Case and Correlation
In this chapter, we present a framework for m-person stochastic games with an infinite state space. Our main purpose is to present a correlated equilibrium theorem proved by Nowak and Raghavan [42] for discounted stochastic games with a measurable state space, where the correlation o
A Remark on Control of Partially Observed Markov Chains
A new state variable is introduced for the problem of controlling a Markov chain under partial observations, which, under a suitably altered probability measure, has a simple evolution
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