Combinatorial Q-Learning for Dou Di Zhu

Published in AIIDE, 2020

Yang You, Liangwei Li, Baisong Guo, Weiming Wang, Cewu Lu

arXiv / PDF / code / BibTeX

Abstract

In this paper, we study a special class of Asian popular card games called Dou Di Zhu, in which two adversarial groups of agents must consider numerous card combinations at each time step, leading to huge number of actions. We propose a novel method to handle combinatorial actions, which we call combinational Q-learning (CQL). We employ a two-stage network to reduce action space and also leverage order-invariant max-pooling operations to extract relationships between primitive actions.

BibTeX

@inproceedings{you2020combinatorial,
  title     = {Combinatorial Q-Learning for Dou Di Zhu},
  author    = {Yang You and Liangwei Li and Baisong Guo and Weiming Wang and Cewu Lu},
  booktitle = {AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE)},
  year      = {2020},
  eprint    = {1901.08925},
  archivePrefix = {arXiv}
}

Recommended citation: You, Y., Li, L., Guo, B., Wang, W., & Lu, C. (2020). Combinatorial Q-Learning for Dou Di Zhu. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 16(1), 301-307.