An Idiosyncrasy of Time-discretization in Reinforcement Learning

Kris De Asis, Richard S. Sutton

Many reinforcement learning algorithms are built on an assumption that an agent interacts with an environment over fixed-duration, discrete time steps. However, physical systems ar…