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

Jianzhun Du; Joseph Futoma; Finale Doshi-Velez

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arXiv · 2006.16210

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

Jianzhun Du·Joseph Futoma·Finale Doshi-Velez

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Abstract

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.

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Jianzhun Du, Joseph Futoma, Finale Doshi-Velez. 2020-06-29. Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs. https://arxiv.org/abs/2006.16210

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