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Generalized Nested Rollout Policy Adaptation with Limited Repetitions
Published 18 Jan 2024 in cs.AI | (2401.10420v1)
Abstract: Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices. We propose to improve on GNRPA by avoiding too deterministic policies that find again and again the same sequence of choices. We do so by limiting the number of repetitions of the best sequence found at a given level. Experiments show that it improves the algorithm for three different combinatorial problems: Inverse RNA Folding, the Traveling Salesman Problem with Time Windows and the Weak Schur problem.
- New lower bounds for schur and weak schur numbers. arXiv preprint arXiv:2112.03175, 2021.
- Wind farm layout optimization using adaptive evolutionary algorithm with monte carlo tree search reinforcement learning. Energy Conversion and Management, 252:115047, 2022.
- Designing RNA secondary structures is hard. Journal of Computational Biology, 27(3), 2020.
- Computer go: An AI oriented survey. Artificial Intelligence, 132(1):39–103, 2001.
- A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1–43, March 2012.
- Bernd Brügmann. Monte Carlo Go. Technical report, Max-Planke-Inst. Phys., Munich, 1993.
- Predicting the structure of large protein complexes using alphafold and monte carlo tree search. Nature communications, 13(1):6028, 2022.
- Tristan Cazenave. Nested Monte-Carlo Search. In Craig Boutilier, editor, IJCAI, pages 456–461, 2009.
- Tristan Cazenave. Nested rollout policy adaptation with selective policies. In CGW at IJCAI 2016, 2016.
- Tristan Cazenave. Generalized nested rollout policy adaptation. In Monte Carlo Search at IJCAI, 2020.
- Monte Carlo inverse folding. In Monte Carlo Search at IJCAI, 2020.
- Monte carlo vehicle routing. In ATT at ECAI, 2020.
- Policy adaptation for vehicle routing. AI Communications, 2021.
- Monte Carlo graph coloring. In Monte Carlo Search at IJCAI, 2020.
- Stabilized nested rollout policy adaptation. In Monte Carlo Search at IJCAI, 2020.
- Application of the nested rollout policy adaptation algorithm to the traveling salesman problem with time windows. In Learning and Intelligent Optimization - 6th International Conference, LION 6, pages 42–54, 2012.
- Rémi Coulom. Efficient selectivity and backup operators in Monte-Carlo tree search. In H. Jaap van den Herik, Paolo Ciancarini, and H. H. L. M. Donkers, editors, Computers and Games, 5th International Conference, CG 2006, Turin, Italy, May 29-31, 2006. Revised Papers, volume 4630 of Lecture Notes in Computer Science, pages 72–83. Springer, 2006.
- Rémi Coulom. Computing elo ratings of move patterns in the game of Go. ICGA Journal, 30(4):198–208, 2007.
- Monte carlo search algorithms for network traffic engineering. In ECML PKDD, volume 12978 of LNCS, pages 486–501, 2021.
- Warm-starting nested rollout policy adaptation with optimal stopping. In AAAI 2023, pages 12381–12389. AAAI Press, 2023.
- Algorithm and knowledge engineering for the tsptw problem. In Computational Intelligence in Scheduling (SCIS), 2013 IEEE Symposium on, pages 44–51. IEEE, 2013.
- Monte-Carlo tree search for logistics. In Commercial Transport, pages 427–440. Springer International Publishing, 2016.
- Monte-Carlo tree search for 3d packing with object orientation. In KI 2014: Advances in Artificial Intelligence, pages 285–296. Springer International Publishing, 2014.
- Solving physical traveling salesman problems with policy adaptation. In Computational Intelligence and Games (CIG), 2014 IEEE Conference on, pages 1–8. IEEE, 2014.
- Monte-Carlo tree search for the multiple sequence alignment problem. In Proceedings of the Eighth Annual Symposium on Combinatorial Search, SOCS 2015, pages 9–17. AAAI Press, 2015.
- Monkey business: Reinforcement learning meets neighborhood search for virtual network embedding. Computer Networks, 216:109204, 2022.
- Rfam 14: expanded coverage of metagenomic, viral and microrna families. Nucleic Acids Research, 49(D1):D192–D200, 2021.
- Bandit based Monte-Carlo planning. In 17th European Conference on Machine Learning (ECML’06), volume 4212 of LNCS, pages 282–293. Springer, 2006.
- Viennarna package 2.0. Algorithms for molecular biology, 6:1–14, 2011.
- Combining UCT and Nested Monte Carlo Search for single-player general game playing. IEEE Transactions on Computational Intelligence and AI in Games, 2(4):271–277, 2010.
- Fernando Portela. An unexpectedly effective Monte Carlo technique for the RNA inverse folding problem. BioRxiv, page 345587, 2018.
- The vehicle routing problem with time windows part ii: genetic search. INFORMS journal on Computing, 8(2):165–172, 1996.
- Optimization of the Nested Monte-Carlo algorithm on the traveling salesman problem with time windows. In EvoApplications, volume 6625 of LNCS, pages 501–510. Springer, 2011.
- Christopher D. Rosin. Nested rollout policy adaptation for Monte Carlo Tree Search. In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pages 649–654, 2011.
- Comparing search algorithms on the retrosynthesis problem. In AI to Accelerate Science and Engineering at AAAI 2023. 2023.
- Generalized nested rollout policy adaptation with dynamic bias for vehicle routing. In AI for Transportation at AAAI, 2022.
- Mastering the game of go with deep neural networks and tree search. Nature, 529:484–489, 2016.
- Mastering chess and shogi by self-play with a general reinforcement learning algorithm. CoRR, abs/1712.01815, 2017.
- Attention is all you need. Advances in neural information processing systems, 30, 2017.
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