Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2021
Abstract
In many real-world scenarios, a team of agents must coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e.g., Counterfactual Regret Minimization (CFR). To address this problem, we propose a new framework of CFR: CFR-MIX. Firstly, we propose a new strategy representation that represents a joint action strategy using individual strategies of all agents and a consistency relationship to maintain the cooperation between agents. To compute the equilibrium with individual strategies under the CFR framework, we transform the consistency relationship between strategies to the consistency relationship between the cumulative regret values. Furthermore, we propose a novel decomposition method over cumulative regret values to guarantee the consistency relationship between the cumulative regret values. Finally, we introduce our new algorithm CFR-MIX which employs a mixing layer to estimate cumulative regret values of joint actions as a non-linear combination of cumulative regret values of individual actions. Experimental results show that CFR-MIX outperforms existing algorithms on various games significantly.
Keywords
Security and privacy, noncooperative games, Computational sustainability
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21): Montreal, August 19-26
First Page
3663
Last Page
3669
ISBN
9780999241196
Identifier
10.24963/ijcai.2021/504
Publisher
IJCAI
City or Country
Montreal
Citation
LI, Shuxin; ZHANG, Youzhi; WANG, Xinrun; XUE, Wanqi; and AN, Bo.
CFR-MIX: Solving imperfect information extensive-form games with combinatorial action space. (2021). Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21): Montreal, August 19-26. 3663-3669.
Available at: https://ink.library.smu.edu.sg/sis_research/9138
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.24963/ijcai.2021/504