An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control

• URL: http://arxiv.org/abs/2410.05163v3
• Date: Tue, 13 May 2025 03:30:44 GMT
• Title: An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
• Authors: Mengjian Hua, Mathieu Laurière, Eric Vanden-Eijnden,
• Abstract summary: We present a novel on-policy algorithm for solving optimal control (SOC) problems.<n>By leveraging the Girsanov theorem, our method directly computes on-policy gradients of the SOC objective without expensive backpropagation through differential equations or adjoint problem solutions.<n> Experimental results demonstrate substantial improvements in both computational speed and memory efficiency compared to existing approaches.
• Score: 14.832859803172846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel on-policy algorithm for solving stochastic optimal control (SOC) problems. By leveraging the Girsanov theorem, our method directly computes on-policy gradients of the SOC objective without expensive backpropagation through stochastic differential equations or adjoint problem solutions. This approach significantly accelerates the optimization of neural network control policies while scaling efficiently to high-dimensional problems and long time horizons. We evaluate our method on classical SOC benchmarks as well as applications to sampling from unnormalized distributions via Schr\"odinger-F\"ollmer processes and fine-tuning pre-trained diffusion models. Experimental results demonstrate substantial improvements in both computational speed and memory efficiency compared to existing approaches.

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