Faster Regret Matching

01/14/2020
by   Dawen Wu, et al.
0

The regret matching algorithm proposed by Sergiu Hart is one of the most powerful iterative methods in finding correlated equilibrium. However, it is possibly not efficient enough, especially in large scale problems. We first rewrite the algorithm in a computationally practical way based on the idea of the regret matrix. Moreover, the rewriting makes the original algorithm more easy to understand. Then by some modification to the original algorithm, we introduce a novel variant, namely faster regret matching. The experiment result shows that the novel algorithm has a speed advantage comparing to the original one.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/01/2020

No-regret learning dynamics for extensive-form correlated and coarse correlated equilibria

Recently, there has been growing interest around less-restrictive soluti...
research
10/03/2019

Bounds for Approximate Regret-Matching Algorithms

A dominant approach to solving large imperfect-information games is Coun...
research
08/18/2019

Geometrical Regret Matching of Mixed Strategies

We argue that the existing regret matchings for equilibrium approximatio...
research
11/06/2018

Composability of Regret Minimizers

Regret minimization is a powerful tool for solving large-scale problems;...
research
02/13/2019

Stable-Predictive Optimistic Counterfactual Regret Minimization

The CFR framework has been a powerful tool for solving large-scale exten...
research
08/02/2022

Unimodal Mono-Partite Matching in a Bandit Setting

We tackle a new emerging problem, which is finding an optimal monopartit...
research
08/02/2022

UniRank: Unimodal Bandit Algorithm for Online Ranking

We tackle a new emerging problem, which is finding an optimal monopartit...

Please sign up or login with your details

Forgot password? Click here to reset