Summary of The Max-min Formulation Of Multi-objective Reinforcement Learning: From Theory to a Model-free Algorithm, by Giseung Park et al.
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
by Giseung Park, Woohyeon Byeon, Seongmin Kim, Elad Havakuk, Amir Leshem, Youngchul Sung
First submitted to arxiv on: 12 Jun 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 The paper tackles multi-objective reinforcement learning, a crucial problem in many real-world scenarios where multiple optimization goals coexist. A max-min framework is employed to prioritize fairness among these objectives, leading to both theoretical advancements and practical model-free algorithm developments. The proposed method outperforms existing baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on finding the best solution when there are multiple things we want to achieve at the same time. Think of it like balancing different goals in a game or a real-life situation. The scientists developed a new way to think about this problem, which they call the “max-min” framework. They also created a special computer algorithm that can solve these multi-objective problems efficiently. Their approach showed significant improvements over current methods. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
