Papers › Continuous-Time Model-Based Reinforcement Learning

Continuous-Time Model-Based Reinforcement Learning

9 Feb 2021arXiv:2102.04764archive 2025-07-28

Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki

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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Model-based Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)model reinforcement-learning

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