| Title | Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Yao, Zhiyuan, Shi, Tianyu, Li, Site, Xie, Yiting, Qin, Yuanyuan, Xie, Xiongjie, Lu, Huan, Zhang, Yan |
| Conference Name | 2022 IEEE Conference on Games (CoG) |
| Keywords | Action Representation, Atmospheric modeling, Axie Infinity, Data models, deterrence, Game AI, Games, Human Behavior, Large-Scale Action Space, pubcrawl, reinforcement learning, resilience, Resiliency, Scalability, Training |
| Abstract | Axie infinity is a complicated card game with a huge-scale action space. This makes it difficult to solve this challenge using generic Reinforcement Learning (RL) algorithms. We propose a hybrid RL framework to learn action representations and game strategies. To avoid evaluating every action in the large feasible action set, our method evaluates actions in a fixed-size set which is determined using action representations. We compare the performance of our method with two baseline methods in terms of their sample efficiency and the winning rates of the trained models. We empirically show that our method achieves an overall best winning rate and the best sample efficiency among the three methods. |
| Notes | ISSN: 2325-4289 |
| DOI | 10.1109/CoG51982.2022.9893589 |
| Citation Key | yao_towards_2022 |
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation