Polynomial-Time Computation of Optimal Correlated Equilibria in
Two-Player Extensive-Form Games with Public Chance Moves and Beyond
- URL: http://arxiv.org/abs/2009.04336v1
- Date: Wed, 9 Sep 2020 14:51:58 GMT
- Title: Polynomial-Time Computation of Optimal Correlated Equilibria in
Two-Player Extensive-Form Games with Public Chance Moves and Beyond
- Authors: Gabriele Farina and Tuomas Sandholm
- Abstract summary: We show that an optimal correlated equilibrium can be computed in time in two-player games with public chance moves.
This results in the biggest positive complexity result surrounding extensive-form correlation in more than a decade.
- Score: 107.14897720357631
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unlike normal-form games, where correlated equilibria have been studied for
more than 45 years, extensive-form correlation is still generally not well
understood. Part of the reason for this gap is that the sequential nature of
extensive-form games allows for a richness of behaviors and incentives that are
not possible in normal-form settings. This richness translates to a
significantly different complexity landscape surrounding extensive-form
correlated equilibria. As of today, it is known that finding an optimal
extensive-form correlated equilibrium (EFCE), extensive-form coarse correlated
equilibrium (EFCCE), or normal-form coarse correlated equilibrium (NFCCE) in a
two-player extensive-form game is computationally tractable when the game does
not include chance moves, and intractable when the game involves chance moves.
In this paper we significantly refine this complexity threshold by showing
that, in two-player games, an optimal correlated equilibrium can be computed in
polynomial time, provided that a certain condition is satisfied. We show that
the condition holds, for example, when all chance moves are public, that is,
both players observe all chance moves. This implies that an optimal EFCE, EFCCE
and NFCCE can be computed in polynomial time in the game size in two-player
games with public chance moves, providing the biggest positive complexity
result surrounding extensive-form correlation in more than a decade.
Related papers
- Barriers to Welfare Maximization with No-Regret Learning [68.66209476382213]
We prove lower bounds for computing a near-optimal $T$-sparse CCE.
In particular, we show that the inapproximability of maximum clique precludes attaining any non-trivial sparsity in time.
arXiv Detail & Related papers (2024-11-04T00:34:56Z) - The Computational Complexity of Single-Player Imperfect-Recall Games [37.550554344575]
We study extensive-form games with imperfect recall, such as the Sleeping Beauty problem or the Absent Driver game.
For such games, two natural equilibrium concepts have been proposed as alternative solution concepts to ex-ante optimality.
arXiv Detail & Related papers (2023-05-28T19:41:25Z) - Learning in Multi-Player Stochastic Games [1.0878040851638]
We consider the problem of simultaneous learning in games with many players in the finite-horizon setting.
While the typical target solution for a game is a Nash equilibrium, this is intractable with many players.
We turn to a different target: algorithms which generate an equilibrium when they are used by all players.
arXiv Detail & Related papers (2022-10-25T19:02:03Z) - Learning Correlated Equilibria in Mean-Field Games [62.14589406821103]
We develop the concepts of Mean-Field correlated and coarse-correlated equilibria.
We show that they can be efficiently learnt in emphall games, without requiring any additional assumption on the structure of the game.
arXiv Detail & Related papers (2022-08-22T08:31:46Z) - Optimal Correlated Equilibria in General-Sum Extensive-Form Games: Fixed-Parameter Algorithms, Hardness, and Two-Sided Column-Generation [78.48747645545944]
We study the problem of finding optimal correlated equilibria of various sorts in extensive-form games.
We introduce a new algorithm for computing optimal equilibria in all three notions.
arXiv Detail & Related papers (2022-03-14T15:21:18Z) - Simple Uncoupled No-Regret Learning Dynamics for Extensive-Form
Correlated Equilibrium [65.64512759706271]
We study the existence of simple, uncoupled no-regret dynamics that converge to correlated equilibria in normal-form games.
We introduce a notion of trigger regret in extensive-form games, which extends that of internal regret in normal-form games.
We give an efficient no-regret algorithm which guarantees with high probability that trigger regrets grow sublinearly in the number of iterations.
arXiv Detail & Related papers (2021-04-04T02:26:26Z) - No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium [76.78447814623665]
We give the first uncoupled no-regret dynamics that converge to correlated equilibria in normal-form games.
We introduce a notion of trigger regret in extensive-form games, which extends that of internal regret in normal-form games.
Our algorithm decomposes trigger regret into local subproblems at each decision point for the player, and constructs a global strategy of the player from the local solutions.
arXiv Detail & Related papers (2020-04-01T17:39:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.