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Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
[article]
2020
arXiv
pre-print
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
arXiv:2006.16210v2
fatcat:ap5ed27aqra37g62ghebwudy6q