Comparison of Reinforcement Learning and Model Predictive Control for Automated Generation of Optimal Control for Dynamic Systems within a Design Space Exploration Framework
This work provides a study of methods for the automated derivation of control strategies for over-actuated systems. For this purpose, Reinforcement Learning (RL) and Model Predictive Control (MPC) approximating the solution of the Optimal Control Problem (OCP) are compared using the example of an ov...
Main Authors: | Patrick Hoffmann, Kirill Gorelik, Valentin Ivanov |
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Format: | Article |
Language: | English |
Published: |
Society of Automotive Engineers of Japan, Inc.
2024-01-01
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Series: | International Journal of Automotive Engineering |
Online Access: | https://www.jstage.jst.go.jp/article/jsaeijae/15/1/15_20244099/_article/-char/ja |
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