Deep Reinforcement Learning Under Signal Temporal Logic Constraints Using Lagrangian Relaxation

Deep Reinforcement Learning Under Signal Temporal Logic Constraints Using Lagrangian Relaxation

Deep reinforcement learning (DRL) has attracted much attention as an approach to solve optimal control problems without mathematical models of systems. On the other hand, in general, constraints may be imposed on optimal control problems. In this study, we consider the optimal control problems with...

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Bibliographic Details
Main Authors: Junya Ikemoto, Toshimitsu Ushio
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Constrained reinforcement learning
deep reinforcement learning
Lagrangian relaxation
signal temporal logic
Online Access:https://ieeexplore.ieee.org/document/9933457/

Internet

https://ieeexplore.ieee.org/document/9933457/

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