Workshop on Stochastic Games Directory

A

Approximating an Absorbing Game Using Collections of Games

Author:
Orin Munk
Institution:
Tel-Aviv University
Co Authors:
Eilon Solan
Event:
Workshop on Stochastic Games: theory and computational aspects
Slideshow:
C

Computing Subgame Perfect Equilibrium Payoffs

Author:
Ben Brooks
Institution:
University of Chicago
Event:
Workshop on Stochastic Games: theory and computational aspects
D

Distributed Asynchronous Stochastic Games

Author:
Hugo Gimbert
Institution:
CNRS, LaBRI, Université de Bordeaux
Event:
Workshop on Stochastic Games: theory and computational aspects
Slideshow:
P

Percolation Games: A bridge between Game Theory and Analysis

Author:
Raimundo Saona
Institution:
Institute of Science and Technology Austria (ISTA)
Co Authors:
Luc Attia and Bruno Ziliotto
Event:
Workshop on Stochastic Games: theory and computational aspects

Policy Improvement for Additive Reward additive transition stochastic games with discounted and average payoff

We give a policy improvement algorithm for two person for zero sum  stochastic games with additive reward and additive transition in both discounted and Cesaro average payoffs.

Author:
TES Raghavan
Institution:
University of Illinois at Chicago (Professor Emeritus)
Co Authors:
Matthew Bourque
Event:
Workshop on Stochastic Games: theory and computational aspects
R

Robust optimization in stochastic games

Author:
Abraham Neyman
Institution:
Hebrew University of Jerusalem
Event:
Workshop on Stochastic Games: theory and computational aspects
Slideshow:
S

Sequential Optimization of CVaR for MDPs is a Stochastic Game: Existence and Computation of Optimal Policies

We study the problem of Conditional Value at Risk (CVaR) optimization for a finite-state Markov Decision Process (MDP) with total discounted costs and the reduction of this problem to a stochastic game with perfect information. The CVaR optimization problem for a finite and infinite-horizon MDP can be reformulated as a zero-sum stochastic game with a compact state space. This game has the following property: while the second player has perfect information including the knowledge of the decision chosen by the first player at the current time instance, the first player does not directly observe the augmented component of the state and does not know current and past decisions chosen by the second player. By using methods of convex analysis, we show that optimal policies exist for this game, and an optimal policy of the first player optimizes CVaR of the total discounted costs. In addition to proving the existence of optimal policies, we formulate algorithms for their computation and prove convergence.

Author:
Eugene Feinberg
Institution:
Stony Brook University
Co Authors:
Rui Ding, Eugene Feinberg
Event:
Workshop on Stochastic Games: theory and computational aspects

Splitting Games over finite sets

Author:
Jérôme Renault
Institution:
Toulouse School of Economics
Co Authors:
Frédéric Koessler, Marie Laclau, and Tristan Tomala
Event:
Workshop on Stochastic Games: theory and computational aspects
Slideshow:

Stackelberg-Pareto Synthesis and Veri cation

Author:
Véronique Bruyère
Institution:
University of Mons
Co Authors:
Jean-François Raskin and Clément Tamines
Event:
Workshop on Stochastic Games: theory and computational aspects
Slideshow:

Stochastic Games from the viewpoint of Computational Complexity Theory

Author:
Kristoffer Arnsfelt Hansen
Institution:
Aarhus University
Event:
Workshop on Stochastic Games: theory and computational aspects
Z

Zero-sum stochastic games with intermittent observation of the state.

Author:
Guillaume Vigeral
Institution:
CEREMADE Université Paris-Dauphine
Co Authors:
Bruno Ziliotto
Event:
Workshop on Stochastic Games: theory and computational aspects
Slideshow: