Skip to yearly menu bar Skip to main content


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

Jianzhun Du · Joseph Futoma · Finale Doshi-Velez

Poster Session 0 #147

Keywords: [ Deep Learning ] [ Online Learning ] [ Algorithms -> Multitask and Transfer Learning; Algorithms ]


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.

Chat is not available.