Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation

Dulac-Arnold et Games have facilitated the rapid development of RL algorithms al. [11] propose to choose actions in a small subset of the in recent years. Card games, as a classical type action space to speed up the action search process. This set of games, also pose many challenges to RL algorithms. The is chosen based on a proper action encoding method which direct applications of generic algorithms [1]-[4] in card games usually relies on prior knowledge. However, the prior human are problematic in many aspects because of the large-scale knowledge for our problem is hard to obtain due to the discrete action space [5]. Prior works have proposed RL diversity of the teams and the strategies. Chandak et al. [12] methods to approach a number of traditional card games, like propose an algorithm to learn action representations from the Texas Hold'em [6]-[8], Mahjong [9], DouDizhu [5], [10], etc. consequences of corresponding actions.