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 |
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IEEE
01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | 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 instances. In this work, we present CusADi , an extension of the casadi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA . We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training. |
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| AbstractList | 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 instances. In this work, we present CusADi , an extension of the casadi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA . We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training. |
| Author | Khazoom, Charles Hong, Seungwoo Kim, Sangbae Jeon, Se Hwan Lee, Ho Jae |
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| SubjectTerms | 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 |
| Title | CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control |
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