Summary of Offline Model-based Optimization Via Policy-guided Gradient Search, by Yassine Chemingui et al.
Offline Model-Based Optimization via Policy-Guided Gradient Search
by Yassine Chemingui, Aryan Deshwal, Trong Nghia Hoang, Janardhan Rao Doppa
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers tackle the problem of offline optimization in various experimental engineering domains, such as protein and aircraft design. They propose a novel approach called “learning-to-search” that reformulates offline optimization as an offline reinforcement learning problem. The method introduces a policy-guided gradient search that learns the best policy for a given surrogate model created from offline data. This approach improves optimization performance on multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in designing things like proteins and aircraft. It’s hard to test these designs without spending too much money or risking damage. The researchers found a new way to do this by looking at the designs and trying different changes to see what works best. They used something called reinforcement learning, which is like training an AI to make good decisions. This method can help us design better things more quickly and accurately. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
