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Axie infinity is a complicated card game with a huge-scale action space. Thismakes it difficult to solve this challenge using generic Reinforcement Learning(RL) algorithms. We propose a hybrid RL framework to learn actionrepresentations and game strategies. To avoid evaluating every action in thelarge feasible action set, our method evaluates actions in a fixed-size setwhich is determined using action representations. We compare the performance ofour method with the other two baseline methods in terms of their sampleefficiency and the winning rates of the trained models. We empirically showthat our method achieves an overall best winning rate and the best sampleefficiency among the three methods.

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