  Soft Actor-Critic is a state-of-the-art reinforcement learning algorithm for
continuous action settings that is not applicable to discrete action settings.
Many important settings involve discrete actions, however, and so here we
derive an alternative version of the Soft Actor-Critic algorithm that is
applicable to discrete action settings. We then show that, even without any
hyperparameter tuning, it is competitive with the tuned model-free
state-of-the-art on a selection of games from the Atari suite.
