Team Correlated Equilibria in Zero-Sum Extensive-Form Games via Tree Decompositions

TL;DR

This paper introduces a novel tree decomposition-based algorithm for efficiently computing correlated equilibria in extensive-form team games, especially under common external information conditions, outperforming previous methods.

Contribution

The paper presents a new algorithm that uses tree decompositions to solve team games more efficiently, handling larger game sizes and complex information structures.

Findings

Achieves state-of-the-art performance on benchmark games.

Reduces problem size to a linear program with fewer constraints.

Handles complex external information structures effectively.

Abstract

Despite the many recent practical and theoretical breakthroughs in computational game theory, equilibrium finding in extensive-form team games remains a significant challenge. While NP-hard in the worst case, there are provably efficient algorithms for certain families of team game. In particular, if the game has common external information, also known as A-loss recall -- informally, actions played by non-team members (i.e., the opposing team or nature) are either unknown to the entire team, or common knowledge within the team -- then polynomial-time algorithms exist (Kaneko & Kline 1995). In this paper, we devise a completely new algorithm for solving team games. It uses a tree decomposition of the constraint system representing each team's strategy to reduce the number and degree of constraints required for correctness (tightness of the mathematical program). Our approach has the bags of the tree decomposition correspond to team-public states. Our algorithm reduces the problem of solving team games to a linear program with at most $O(NW^{w+1})$ nonzero entries in the constraint matrix, where $N$ is the size of the game tree, $w$ is a parameter that depends on the amount of uncommon external information, and $W$ is the treewidth of the tree decomposition. In public-action games, our program size is bounded by the tighter $2^{O(nt)}N$ for teams of $n$ players with $t$ types each. Our algorithm is based on a new way to write a custom, concise tree decomposition, and its fast run time does not assume that the decomposition has small treewidth. Since our algorithm describes the polytope of correlated strategies directly, we get equilibrium finding in correlated strategies for free -- instead of, say, having to run a double oracle algorithm. We show via experiments on a standard suite of games that our algorithm achieves state-of-the-art performance on all benchmark game classes except one.