  Reinforcement learning (RL) methods work in discrete time. In order to apply
RL to inherently continuous problems like robotic control, a specific time
discretization needs to be defined. This is a choice between sparse time
control, which may be easier to train, and finer time control, which may allow
for better ultimate performance. In this work, we propose SusACER, an
off-policy RL algorithm that combines the advantages of different time
discretization settings. Initially, it operates with sparse time discretization
and gradually switches to a fine one. We analyze the effects of the changing
time discretization in robotic control environments: Ant, HalfCheetah, Hopper,
and Walker2D. In all cases our proposed algorithm outperforms state of the art.
