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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1156))

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Abstract

Today planning algorithms are among the most sought after. One of the main such algorithms is Monte Carlo Tree Search. However, this architecture is complex in terms of parallelization and development. We presented possible approximations for the MCTS algorithm, which allowed us to significantly increase the learning speed of the agent.

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References

  1. Sutton, R.S.: Dyna, an integrated architecture for learning, planning, and reacting. SIGART Bull. 2(4), 160–163 (1991)

    Article  Google Scholar 

  2. Coulom, R.: Efficient selectivity and backup operators in monte-carlo tree search. In: International Conference on Computers and Games, pp. 72–83. Springer (2006)

    Google Scholar 

  3. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354 (2017)

    Article  Google Scholar 

  4. Kocsis, L., Szepesvári, C.: Bandit based monte-carlo planning. In: European Conference on Machine Learning, pp. 282–293. Springer (2006)

    Google Scholar 

  5. Gelly, S., Silver, D.: Monte-carlo tree search and rapid action value estimation in computer go. Artif. Intell. 175(11), 1856–1875 (2011)

    Article  MathSciNet  Google Scholar 

  6. Lecarpentier, E., Infantes, G., Lesire, C., Rachelson, E.: Open loop execution of tree-search algorithms. arXiv preprint arXiv:1805.01367 (2018)

  7. Guez, A., Weber, T., Antonoglou, I., Simonyan, K., Vinyals, O., Wierstra, D., Munos, R., Silver, D.: Learning to search with MCTSNets. arXiv preprint arXiv:1802.04697 (2018)

  8. Racanière, S., Weber, T., Reichert, D., Buesing, L., Guez, A., Rezende, D.J., Badia, A.P., Vinyals, O., Heess, N., Li, Y., et al.: Imagination-augmented agents for deep reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 5690–5701 (2017)

    Google Scholar 

  9. Chaslot, G.M.J.-B., Winands, M.H.M., van Den Herik, H.J.: Parallel monte-carlo tree search. In: International Conference on Computers and Games, pp. 60–71. Springer (2008)

    Google Scholar 

  10. Ali Mirsoleimani, S., Plaat, A., van den Herik, J., Vermaseren, J.: A new method for parallel Monte Carlo tree search. arXiv preprint arXiv:1605.04447 (2016)

  11. Schrader, M.-P.B.: gym-sokoban (2018). https://github.com/mpSchrader/gym-sokoban

  12. Edelkamp, S., Gath, M., Greulich, C., Humann, M., Herzog, O., Lawo, M.: Monte-Carlo tree search for logistics. In: Commercial Transport, pp. 427–440. Springer (2016)

    Google Scholar 

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Acknowledgements

The reported study was supported by RFBR, research Projects No. 17-29-07079 and No. 18-29-22047.

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Correspondence to Aleksandr I. Panov .

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Aksenov, K., Panov, A.I. (2020). Approximation Methods for Monte Carlo Tree Search. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_8

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