Stochastic homogenization of nonconvex Hamilton-Jacobi equations: a counterexample

We provide an example of a Hamilton-Jacobi equation in which stochastic homogenization does not occur. The Hamiltonian involved in this example satisfies the standard assumptions of the literature, except that it is not convex

A Tauberian theorem for nonexpansive operators and applications to zero-sum stochastic games

We prove a Tauberian theorem for nonexpansive operators, and apply it to the model of zero-sum stochastic game. Under mild assumptions, we prove that the value of the lambda-discounted game v_{lambda} converges uniformly when lambda goes to 0 if and only if the value of the n-stage game v_n converges uniformly when n goes to infinity. This generalizes the Tauberian theorem of Lehrer and Sorin (1992) to the two-player zero-sum case. We also provide the first example of a stochastic game with public signals on the state and perfect observation of actions, with finite state space, signal sets and action sets, in which for some initial state k_1 known by both players, (v_{lambda}(k_1)) and (v_n(k_1)) converge to distinct limits

The Complexity of POMDPs with Long-run Average Objectives

We study the problem of approximation of optimal values in partially-observable Markov decision processes (POMDPs) with long-run average objectives. POMDPs are a standard model for dynamic systems with probabilistic and nondeterministic behavior in uncertain environments. In long-run average objectives rewards are associated with every transition of the POMDP and the payoff is the long-run average of the rewards along the executions of the POMDP. We establish strategy complexity and computational complexity results. Our main result shows that finite-memory strategies suffice for approximation of optimal values, and the related decision problem is recursively enumerable complete

General limit value in zero-sum stochastic games

Bewley and Kohlberg (1976) and Mertens and Neyman (1981) have proved, respectively, the existence of the asymptotic value and the uniform value in zero-sum stochastic games with finite state space and finite action sets. In their work, the total payoff in a stochastic game is defined either as a Cesaro mean or an Abel mean of the stage payoffs. This paper presents two findings: first, we generalize the result of Bewley and Kohlberg to a more general class of payoff evaluations and we prove with a counterexample that this result is tight. We also investigate the particular case of absorbing games. Second, for the uniform approach of Mertens and Neyman, we provide another counterexample to demonstrate that there is no natural way to generalize the result of Mertens and Neyman to a wider class of payoff evaluations

Constant payoff in zero-sum stochastic games

In a zero-sum stochastic game, at each stage, two adversary players take decisions and receive a stage payoff determined by them and by a random variable representing the state of nature. The total payoff is the discounted sum of the stage payoffs. Assume that the players are very patient and use optimal strategies. We then prove that, at any point in the game, players get essentially the same expected payoff: the payoff is constant. This solves a conjecture by Sorin, Venel and Vigeral (2010). The proof relies on the semi-algebraic approach for discounted stochastic games introduced by Bewley and Kohlberg (1976), on the theory of Markov chains with rare transitions, initiated by Friedlin and Wentzell (1984), and on some variational inequalities for value functions inspired by the recent work of Davini, Fathi, Iturriaga and Zavidovique (2016