Reinforcement learning in continuous-time and space
Cagatay Yildiz
2022 Invited talk
in
Workshop: Continuous Time Perspectives in Machine Learning
in
Workshop: Continuous Time Perspectives in Machine Learning
Abstract
In this talk, we will introduce a continuous-time reinforcement learning (CTRL) framework. Our talk starts with a categorization of RL problems and naturally motivates a continuous-time perspective to RL. We then introduce a model-based CTRL approach, which solves physical control tasks using neural ordinary differential equations as a sub-routine. We conclude by briefly introducing recent approaches to CTRL.
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