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Title: Efficiently Solving Turn-Taking Stochastic Games with Extensive-Form Correlation
We study equilibrium computation with extensive-form correlation in two-player turn-taking stochastic games. Our main results are two-fold: (1) We give an algorithm for computing a Stackelberg extensive-form correlated equilibrium (SEFCE), which runs in time polynomial in the size of the game, as well as the number of bits required to encode each input number. (2) We give an efficient algorithm for approximately computing an optimal extensive-form correlated equilibrium (EFCE) up to machine precision, i.e., the algorithm achieves approximation error 饾渶 in time polynomial in the size of the game, as well as log(1/饾渶). Our algorithm for SEFCE is the first polynomial-time algorithm for equilibrium computation with com- mitment in such a general class of stochastic games. Existing algorithms for SEFCE typically make stronger assumptions such as no chance moves, and are designed for extensive-form games in the less succinct tree form. Our algorithm for approximately optimal EFCE is, to our knowledge, the first algorithm that achieves 3 desiderata simultaneously: approximate optimality, polylogarithmic dependency on the approximation error, and compatibility with stochastic games in the more succinct graph form. Existing algorithms achieve at most 2 of these desiderata, often also relying on additional technical assumptions.  more » « less
Award ID(s):
2307106
NSF-PAR ID:
10477444
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the 24th ACM Conference on Economics and Computation
Page Range / eLocation ID:
1161 to 1186
Format(s):
Medium: X
Location:
London United Kingdom
Sponsoring Org:
National Science Foundation
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