Summary of Primitive Agentic First-order Optimization, by R. Sala
Primitive Agentic First-Order Optimization
by R. Sala
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY); 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 The study presents a proof-of-concept combining primitive state representations and agent-environment interactions as first-order optimizers for budget-limited optimization. The approach utilizes reinforcement learning over training instances of an optimization problem class to approximate optimal policies for sequential update selection of algorithmic iteration steps in low-dimensional partial state representations considering progress and resource use. This method outperforms conventional optimal algorithms with optimized hyperparameters on unseen instances of quadratic optimization problem classes, showcasing the potential for elementary RL methods combined with succinct partial state representations as heuristics for managing complexity in RL-based optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a new way to solve complex optimization problems quickly and efficiently. It combines simple ideas from artificial intelligence (AI) called reinforcement learning with easy-to-understand information about the problem being solved. This combination helps machines make better decisions by considering how much progress has been made and how many resources are used. The results show that this approach works well, even beating more complex algorithms designed specifically for these problems. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
