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About Combining Reinforcement Learning and Constraint Programming For Combinatorial Optimization
This document proposes combining reinforcement learning and constraint programming to solve combinatorial optimization problems. It presents a hybrid approach that uses dynamic programming as a bridge between reinforcement learning and constraint programming. Specifically, it trains reinforcement learning models on randomly generated problem instances to learn effective constraint programming branching strategies. These learned strategies are then integrated into different constraint programming search algorithms. Experimental results on the traveling salesman problem with time windows and portfolio optimization problems show the framework outperforms standalone reinforcement learning and constraint programming approaches.
Detailed Information
| Author: | ['Lolo Cha9chou9'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 20 |
| Language: | English |
| Format: | |
| Price: | FREE |
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