CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 10; H. 2; S. 899 - 906
Hauptverfasser:	Jeon, Se Hwan, Hong, Seungwoo, Lee, Ho Jae, Khazoom, Charles, Kim, Sangbae
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.02.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Closed form solutions Codes Controllers Exact solutions Graphics processing units Optimal control Optimization Optimization and optimal control Reinforcement learning Robot programming Software tools software tools for robot programming Trajectory
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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