  While most current research in Reinforcement Learning (RL) focuses on
improving the performance of the algorithms in controlled environments, the use
of RL under constraints like those met in the video game industry is rarely
studied. Operating under such constraints, we propose Hybrid SAC, an extension
of the Soft Actor-Critic algorithm able to handle discrete, continuous and
parameterized actions in a principled way. We show that Hybrid SAC can
successfully solve a highspeed driving task in one of our games, and is
competitive with the state-of-the-art on parameterized actions benchmark tasks.
We also explore the impact of using normalizing flows to enrich the
expressiveness of the policy at minimal computational cost, and identify a
potential undesired effect of SAC when used with normalizing flows, that may be
addressed by optimizing a different objective.
