We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number...
Abstract. The synthesis of a reactive system with respect to an ωregular specification requires the solution of a graph game. Such games have been extended in two natural ways. F...
Krishnendu Chatterjee, Thomas A. Henzinger, Floria...
The description of resources in game semantics has never achieved the simplicity and precision of linear logic, because of the misleading conception that linear logic is more prim...
Typically agent evaluation is done through Monte Carlo estimation. However, stochastic agent decisions and stochastic outcomes can make this approach inefficient, requiring many s...
Michael H. Bowling, Michael Johanson, Neil Burch, ...
In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...