Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Intro

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1 of 19

Intro

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Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

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

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