Completed
PORT: Results
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Classroom Contents
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
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- 1 Intro
- 2 Search-based approaches
- 3 End-to-end learning-based approaches
- 4 Solving COPs by searching and learning Taking the best of the two worlds
- 5 Proposed approach
- 6 DP notation
- 7 From DP to CP
- 8 Proposed Framework
- 9 DL, RL and Search Architecture
- 10 Illustration on TSP
- 11 Link To RL environment
- 12 Constraint programming search
- 13 Adding Constraints
- 14 TSPTW: A DP model
- 15 TSPTW: Results
- 16 4- Moments Portfolio Optimization
- 17 PORT: Results
- 18 Conclusion and perspectives
- 19 Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
