  We present two elegant solutions for modeling continuous-time dynamics, in a
novel model-based reinforcement learning (RL) framework for semi-Markov
decision processes (SMDPs), using neural ordinary differential equations
(ODEs). Our models accurately characterize continuous-time dynamics and enable
us to develop high-performing policies using a small amount of data. We also
develop a model-based approach for optimizing time schedules to reduce
interaction rates with the environment while maintaining the near-optimal
performance, which is not possible for model-free methods. We experimentally
demonstrate the efficacy of our methods across various continuous-time domains.
