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Summary of A Simulation-free Deep Learning Approach to Stochastic Optimal Control, by Mengjian Hua et al.


A Simulation-Free Deep Learning Approach to Stochastic Optimal Control

by Mengjian Hua, Matthieu Laurière, Eric Vanden-Eijnden

First submitted to arxiv on: 7 Oct 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Optimization and Control (math.OC)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
Our proposed algorithm tackles generic problems in stochastic optimal control (SOC) without requiring simulation-based adjoint solutions. Instead, we employ Girsanov theorem to directly calculate the SOC objective’s gradient on-policy, eliminating the need for expensive back-propagation through stochastic differential equations (SDEs). This enables efficient optimization of neural network-parameterized control policies, even in high-dimensional and long-time-horizon scenarios. We demonstrate our approach’s efficacy across various applications, including standard SOC problems, sampling from unnormalized distributions via Schrödinger-Föllmer processes, and fine-tuning pre-trained diffusion models. Our method outperforms existing methods in both computing time and memory efficiency.
Low GrooveSquid.com (original content) Low Difficulty Summary
We’re working on a new way to solve big optimization problems without needing to simulate lots of scenarios. Instead, we use a special math trick called Girsanov theorem to find the right solution. This makes it much faster and more efficient than before. We tested this method in different areas, like finding good control policies or fine-tuning existing models. It did better than previous methods in terms of time and memory usage.

Keywords

* Artificial intelligence  * Fine tuning  * Neural network  * Optimization