NeurIPS 2020poster74 citations
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
Jianzhun Du, Joseph Futoma, Finale Doshi-Velez
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.
BibTeX
@inproceedings{NEURIPS2020_e562cd9c,
author = {Du, Jianzhun and Futoma, Joseph and Doshi-Velez, Finale},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {19805--19816},
publisher = {Curran Associates, Inc.},
title = {Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/e562cd9c0768d5464b64cf61da7fc6bb-Paper.pdf},
volume = {33},
year = {2020}
}