  Reinforcement learning is well-studied under discrete actions. Integer
actions setting is popular in the industry yet still challenging due to its
high dimensionality. To this end, we study reinforcement learning under integer
actions by incorporating the Soft Actor-Critic (SAC) algorithm with an integer
reparameterization. Our key observation for integer actions is that their
discrete structure can be simplified using their comparability property. Hence,
the proposed integer reparameterization does not need one-hot encoding and is
of low dimensionality. Experiments show that the proposed SAC under integer
actions is as good as the continuous action version on robot control tasks and
outperforms Proximal Policy Optimization on power distribution systems control
tasks.
