We study a two-player, zero-sum, stochastic game with incomplete information on one side in which the players are allowed to play more and more frequently. The informed player observes the realization of a Markov chain on which the payoffs depend, while the non-informed player only observes his opponent’s actions. We show the existence of a limit value as the time span between two consecutive s...

We study a two-player, zero-sum, stochastic game with incomplete information on one side in which the players are allowed to play more and more frequently. The informed player observes the realization of a Markov chain on which the payoffs depend, while the non-informed player only observes his opponent’s actions. We show the existence of a limit value as the time span between two consecutive s...

Game theory is a widely used formal model for studying strategical interactions between agents. Boolean games [8] are two players, zero-sum static games where players’ utility functions are binary and described by a single propositional formula, and the strategies available to a player consist of truth assignments to each of a given set of propositional variables (the variables controlled by th...

Game theory is a widely used formal model for studying strategical interactions between agents. Boolean games [7] are two players, zero-sum static games where player’s utility functions are binary and described by a single propositional formula, and the strategies available to a player consist of truth assignments to each of a given set of propositional variables (the variables controlled by th...

In many multiagent environments, a designer has some, but limited control over the game being played. In this paper, we formalize this by considering incompletely specified games, in which some entries of the payoff matrices can be chosen from a specified set. We show that it is NP-hard for the designer to make this choices optimally, even in zero-sum games. In fact, it is already intractable t...

We consider reinforcement learning algorithms in normal form games. Using two-timescales stochastic approximation, we introduce a modelfree algorithm which is asymptotically equivalent to the smooth fictitious play algorithm, in that both result in asymptotic pseudotrajectories to the flow defined by the smooth best response dynamics. Both of these algorithms are shown to converge almost surely...

We consider reinforcement learning algorithms in normal form games. Using two-time-scales stochastic approximation, we introduce a modelfree algorithm which is asymptotically equivalent to the smooth fictitious play algorithm, in that both result in asymptotic pseudotrajectories to the flow defined by the smooth best response dynamics. Both of these algorithms are shown to converge almost surel...

In this section we study games with only two players. We also restrict attention to the case where the interests of the players are completely antagonistic: at the end of the game, one player gains some amount, while the other loses the same amount. These games are called “two person zero sum games”. While in most economics situations the interests of the players are neither in strong conflict ...