Summary of Thresholded Lexicographic Ordered Multiobjective Reinforcement Learning, by Alperen Tercan and Vinayak S. Prabhu
Thresholded Lexicographic Ordered Multiobjective Reinforcement Learning
by Alperen Tercan, Vinayak S. Prabhu
First submitted to arxiv on: 24 Aug 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 proposes a novel approach to addressing lexicographic multi-objective problems in Reinforcement Learning, which is essential for real-life scenarios where objectives have a hierarchical importance order. The authors identify shortcomings in existing heuristics and develop the Lexicographic Projection Optimization (LPO) algorithm to improve practical performance. By optimizing policies using LPO, the proposed approach has theoretical guarantees and can potentially overcome limitations such as not reaching goal states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computer science called “lexicographic multi-objective problems”. It’s like having multiple goals that are important in different ways, and you need to figure out how to achieve them. The researchers found that old methods didn’t work well and created a new way called Lexicographic Projection Optimization (LPO) to help with this problem. They tested their method on some examples and showed it can make a big difference. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
