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931. Learning to solve combinatorial optimization problems with a decision tree
Invited abstract in session MD-3: (Deep) Reinforcement Learning for Combinatorial Optimization 2, stream Data Science Meets Optimization.
Monday, 14:30-16:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Kevin Tierney
|
| Decision and Operation Technologies, Bielefeld University |
Abstract
Deep reinforcement learning has made incredible strides in solving combinatorial optimization problems (COPs) and nearly outperforms the current state-of-the-art OR heuristics on several problems. However, a key drawback of deep neural network approaches is that they are not interpretable, that is, it is essentially impossible to understand how they actually solve optimization problems. To this end, I introduce a fully interpretable mechanism for generating interpretable models for solving COPs using a decision tree. The method harnesses a pairwise ranking mechanism to construct solutions, thus allowing it to learn to solve various instance sizes with a single model. To train the decision tree, I introduce an end-to-end learning technique to generate trees that are customized to specific datasets and show the effectiveness of this technique experimentally on several COPs.
Keywords
- Machine Learning
- Transportation
- Artificial Intelligence
Status: accepted
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