Continuous-Time Model-Based Reinforcement Learning

• URL: http://arxiv.org/abs/2102.04764v2
• Date: Wed, 10 Feb 2021 08:46:17 GMT
• Title: Continuous-Time Model-Based Reinforcement Learning
• Authors: \c{C}a\u{g}atay Y{\i}ld{\i}z, Markus Heinonen, and Harri L\"ahdesm\"aki
• Abstract summary: We propose a continuous-time MBRL framework based on a novel actor-critic method. We implement and test our method on a new ODE-RL suite that explicitly solves continuous-time control systems.
• Score: 4.427447378048202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time. To avoid time-discretization approximation of the underlying process, we propose a continuous-time MBRL framework based on a novel actor-critic method. Our approach also infers the unknown state evolution differentials with Bayesian neural ordinary differential equations (ODE) to account for epistemic uncertainty. We implement and test our method on a new ODE-RL suite that explicitly solves continuous-time control systems. Our experiments illustrate that the model is robust against irregular and noisy data, is sample-efficient, and can solve control problems which pose challenges to discrete-time MBRL methods.

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