Abstract. There are only a few learning algorithms applicable to stochastic dynamic games. Learning in games is generally difficult because of the non-stationary environment in which each decision maker aims to learn its optimal decisions with minimal information in the presence of the other decision makers who are also learning. In the case of dynamic games, learning is more challenging becaus...

The optimal value computation for turned-based stochastic games with reachability objectives, also known as simple stochastic games, is one of the few problems in NP∩coNP which are not known to be in P. However, there are some cases where these games can be easily solved, as for instance when the underlying graph is acyclic. In this work, we try to extend this tractability to several classes of...

Significant progress has been made recently in the following two lines of research in the intersection of AI and game theory: (1) the computation of optimal strategies to commit to (Stackelberg strategies), and (2) the computation of correlated equilibria of stochastic games. In this paper, we unite these two lines of research by studying the computation of Stackelberg strategies in stochastic ...

Many verification, planning, and control problems can be modeled as games played on state-transition graphs by one or two players whose conflicting goals are to form a path in the graph. The focus here is on simple stochastic parity games, that is, two-player games with turn-based probabilistic transitions and ω-regular objectives formalized as parity (Rabin chain) winning conditions. An effici...

In this lecture we will review several topics that are extensively used in the study of n-player stochastic games. These tools were used in the proof of several results on non zero-sum stochastic games. Most of the results that are presented here appeared in Vieille (1997a,b), and some appeared in Solan (1998, 1999). The first main issue is Markov chains where the transition rule is a Puiseux p...