Summary of Optimal Control Of Agent-based Dynamics Under Deep Galerkin Feedback Laws, by Frederik Kelbel
Optimal Control of Agent-Based Dynamics under Deep Galerkin Feedback Laws
by Frederik Kelbel
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of high-dimensional control problems, which have long been an obstacle in dynamic programming. The authors propose a novel solution using Deep Neural Networks (DNNs) to address this issue. Specifically, they investigate the sampling issues affecting the Deep Galerkin Method and introduce a drift relaxation-based sampling approach to mitigate high-variance policy approximations. This approach is validated on mean-field control problems, including variations of the Sznajd and Hegselmann-Krause opinion dynamics models. The resulting policies show significant cost reductions compared to manually optimised control functions and outperform the Deep FBSDE approach in Linear-Quadratic Regulator problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a long-standing problem in computer science called “high-dimensional control”. It’s like trying to solve a really hard puzzle that gets harder and harder as you go along. The authors use special kinds of math models called Deep Neural Networks to make it easier. They tested their idea on some complex problems, including ones about how people agree or disagree with each other. Their solution worked better than other methods at solving these problems and could be used in real-life applications. |
