DeepAI AI Chat
Log In Sign Up

Solving imperfect-information games via exponential counterfactual regret minimization

08/06/2020
by   Huale Li, et al.
Harbin Institute of Technology
0

Two agents' decision-making problems can be modeled as the game with two players, and a Nash equilibrium is the basic solution conception representing good play in games. Counterfactual regret minimization (CFR) is a popular method to solve Nash equilibrium strategy in two-player zero-sum games with imperfect information. The CFR and its variants have successfully addressed many problems in this field. However, the convergence of the CFR methods is not fast, since they solve the strategy by iterative computing. To some extent, this further affects the solution performance. In this paper, we propose a novel CFR based method, exponential counterfactual regret minimization, which also can be called as ECFR. Firstly, we present an exponential reduction technique for regret in the process of the iteration. Secondly, we prove that our method ECFR has a good theoretical guarantee of convergence. Finally, we show that, ECFR can converge faster compared with the prior state-of-the-art CFR based methods in the experiment.

READ FULL TEXT
01/30/2020

Fictitious Play Outperforms Counterfactual Regret Minimization

We compare the performance of two popular iterative algorithms, fictitio...
09/12/2016

Reduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning

Counterfactual Regret Minimization (CFR) is the most popular iterative a...
03/08/2021

Model-Free Online Learning in Unknown Sequential Decision Making Problems and Games

Regret minimization has proved to be a versatile tool for tree-form sequ...
09/10/2020

RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning

Counterfactual regret minimization (CFR) is a popular method to deal wit...
10/15/2021

Combining Counterfactual Regret Minimization with Information Gain to Solve Extensive Games with Imperfect Information

Counterfactual regret Minimization (CFR) is an effective algorithm for s...
04/24/2019

Solving zero-sum extensive-form games with arbitrary payoff uncertainty models

Modeling strategic conflict from a game theoretical perspective involves...
09/10/2018

Learning in time-varying games

In this paper, we examine the long-term behavior of regret-minimizing ag...