  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 the other 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.

Artificial Intelligence