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

• URL: http://arxiv.org/abs/2006.16210v2
• Date: Sun, 25 Oct 2020 05:55:05 GMT
• Title: Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
• Authors: Jianzhun Du, Joseph Futoma, Finale Doshi-Velez
• Abstract summary: We present two solutions for modeling continuous-time dynamics 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 experimentally demonstrate the efficacy of our methods across various continuous-time domains.
• Score: 30.36381338938319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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