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We present two elegant solutions for modeling continuous-time dynamics, in anovel model-based reinforcement learning (RL) framework for semi-Markovdecision processes (SMDPs), using neural ordinary differential equations(ODEs). Our models accurately characterize continuous-time dynamics and enableus to develop high-performing policies using a small amount of data. We alsodevelop a model-based approach for optimizing time schedules to reduceinteraction rates with the environment while maintaining the near-optimalperformance, which is not possible for model-free methods. We experimentallydemonstrate the efficacy of our methods across various continuous-time domains.

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs