Poster

Continuous-time Model-based Reinforcement Learning

Cagatay Yildiz · Markus Heinonen · Harri Lähdesmäki

Keywords: [ Reinforcement Learning and Planning ]

[ Abstract ]
[ Paper ]
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Wed 21 Jul 9 a.m. PDT — 11 a.m. PDT
 
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Wed 21 Jul 7 a.m. PDT — 8 a.m. PDT

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, and can solve classic control problems in a sample-efficient manner.

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