1 research outputs found
Automated Construction of Bounded-Loss Imperfect-Recall Abstractions in Extensive-Form Games
Extensive-form games (EFGs) model finite sequential interactions between
players. The amount of memory required to represent these games is the main
bottleneck of algorithms for computing optimal strategies and the size of these
strategies is often impractical for real-world applications. A common approach
to tackle the memory bottleneck is to use information abstraction that removes
parts of information available to players thus reducing the number of decision
points in the game. However, existing information-abstraction techniques are
either specific for a particular domain, they do not provide any quality
guarantees, or they are applicable to very small subclasses of EFGs. We present
domain-independent abstraction methods for creating imperfect recall
abstractions in extensive-form games that allow computing strategies that are
(near) optimal in the original game. To this end, we introduce two novel
algorithms, FPIRA and CFR+IRA, based on fictitious play and counterfactual
regret minimization. These algorithms can start with an arbitrary domain
specific, or the coarsest possible, abstraction of the original game. The
algorithms iteratively detect the missing information they require for
computing a strategy for the abstract game that is (near) optimal in the
original game. This information is then included back into the abstract game.
Moreover, our algorithms are able to exploit imperfect-recall abstractions that
allow players to forget even history of their own actions. However, the
algorithms require traversing the complete unabstracted game tree. We
experimentally show that our algorithms can closely approximate Nash
equilibrium of large games using abstraction with as little as 0.9% of
information sets of the original game. Moreover, the results suggest that
memory savings increase with the increasing size of the original games