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Finite approximations in discrete-time stochastic control quantized models and asymptotic optimality introduction and summary
(Birkhäuser Basel, 2018)
Control and optimization of dynamical systems in the presence of stochastic uncertainty is a mature field with a large range of applications. A comprehensive treatment of such problems can be found in excellent books and ...
Prelude to part I
(Springer, 2018)
Part I involves classical stochastic control problems, with a single decision maker acting repeatedly over time with its information set growing at each time stage.
Asymptotic optimality of finite models for witsenhausen’s counterexample and beyond
(Birkhäuser Basel, 2018)
In this chapter, we study the approximation of Witsenhausen’s counterexample and the Gaussian relay channel problem by using the results of the previous chapter. In particular, our goal is to establish that finite models ...
Prelude to part II
(Springer, 2018)
In Part II, we focus on decentralized stochastic control problems and their applications. In Chapter 8, we present our results on the finite model approximation of multi-agent stochastic control problems (team decision ...
Approximate markov-nash equilibria for discrete-time risk-sensitive mean-field games
(Informs, 2020-11)
In this paper, we study a class of discrete-time mean-field games under the infinite-horizon risk-sensitive optimality criterion. Risk sensitivity is introduced for each agent (player) via an exponential utility function. ...
Approximate nash equilibria in partially observed stochastic games with mean-field interactions
(Informs, 2019-08)
Establishing the existence of Nash equilibria for partially observed stochastic dynamic games is known to be quite challenging, with the difficulties stemming from the noisy nature of the measurements available to individual ...
Asymptotic optimality of finite model approximations for partially observed markov decision processes with discounted cost
(IEEE, 2020-01)
We consider finite model approximations of discrete-time partially observed Markov decision processes (POMDPs) under the discounted cost criterion. After converting the original partially observed stochastic control problem ...
A topology for team policies and existence of optimal team policies in stochastic team theory
(IEEE, 2020-01)
In this paper, we establish the existence of team-optimal policies for static teams and a class of sequential dynamic teams. We first consider the static team problems and show the existence of optimal policies under certain ...
Finite-state approximations to discounted and average cost constrained Markov decision processes
(IEEE, 2019-07)
In this paper, we consider the finite-state approximation of a discrete-time constrained Markov decision process (MDP) under the discounted and average cost criteria. Using the linear programming formulation of the constrained ...
Independently randomized symmetric policies are optimal for exchangeable stochastic teams with infinitely many decision makers
(IEEE, 2020-12-14)
We study stochastic team (known also as decentralized stochastic control or identical interest stochastic game) problems with large or countably infinite number of decision makers, and characterize existence and structural ...
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