Mastering the game of Go without human knowledge (2024)

References

  1. Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009)

  2. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015)

    Article CAS ADS Google Scholar

  3. Krizhevsky, A., Sutskever, I. & Hinton, G. ImageNet classification with deep convolutional neural networks. In Adv. Neural Inf. Process. Syst. Vol. 25 (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q. ) 1097–1105 (2012)

  4. He, K., Zhang, X., Ren, S . & Sun, J. Deep residual learning for image recognition. In Proc. 29th IEEE Conf. Comput. Vis. Pattern Recognit. 770–778 (2016)

  5. Hayes-Roth, F., Waterman, D. & Lenat, D. Building Expert Systems (Addison-Wesley, 1984)

  6. Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)

    Article CAS ADS Google Scholar

  7. Guo, X., Singh, S. P., Lee, H., Lewis, R. L. & Wang, X. Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning. In Adv. Neural Inf. Process. Syst. Vol. 27 (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q. ) 3338–3346 (2014)

  8. Mnih, V . et al. Asynchronous methods for deep reinforcement learning. In Proc. 33rd Int. Conf. Mach. Learn. Vol. 48 (eds Balcan, M. F. & Weinberger, K. Q. ) 1928–1937 (2016)

  9. Jaderberg, M . et al. Reinforcement learning with unsupervised auxiliary tasks. In 5th Int. Conf. Learn. Representations (2017)

  10. Dosovitskiy, A. & Koltun, V. Learning to act by predicting the future. In 5th Int. Conf. Learn. Representations (2017)

  11. Man´dziuk, J. in Challenges for Computational Intelligence ( Duch, W. & Man´dziuk, J. ) 407–442 (Springer, 2007)

  12. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)

    Article CAS ADS Google Scholar

  13. Coulom, R. Efficient selectivity and backup operators in Monte-Carlo tree search. In 5th Int. Conf. Computers and Games (eds Ciancarini, P. & van den Herik, H. J. ) 72–83 (2006)

  14. Kocsis, L. & Szepesvári, C. Bandit based Monte-Carlo planning. In 15th Eu. Conf. Mach. Learn. 282–293 (2006)

  15. Browne, C. et al. A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4, 1–49 (2012)

    Article Google Scholar

  16. f*ckushima, K. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980)

    Article CAS Google Scholar

  17. LeCun, Y. & Bengio, Y. in The Handbook of Brain Theory and Neural Networks Ch. 3 (ed. Arbib, M. ) 276–278 (MIT Press, 1995)

  18. Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc. 32nd Int. Conf. Mach. Learn. Vol. 37 448–456 (2015)

  19. Hahnloser, R. H. R., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J. & Seung, H. S. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000)

    Article CAS ADS Google Scholar

  20. Howard, R. Dynamic Programming and Markov Processes (MIT Press, 1960)

  21. Sutton, R . & Barto, A. Reinforcement Learning: an Introduction (MIT Press, 1998)

  22. Bertsekas, D. P. Approximate policy iteration: a survey and some new methods. J. Control Theory Appl. 9, 310–335 (2011)

    Article MathSciNet Google Scholar

  23. Scherrer, B. Approximate policy iteration schemes: a comparison. In Proc. 31st Int. Conf. Mach. Learn. Vol. 32 1314–1322 (2014)

  24. Rosin, C. D. Multi-armed bandits with episode context. Ann. Math. Artif. Intell. 61, 203–230 (2011)

    Article MathSciNet Google Scholar

  25. Coulom, R. Whole-history rating: a Bayesian rating system for players of time-varying strength. In Int. Conf. Comput. Games (eds van den Herik, H. J., Xu, X . Ma, Z . & Winands, M. H. M. ) Vol. 5131 113–124 (Springer, 2008)

  26. Laurent, G. J., Matignon, L. & Le Fort-Piat, N. The world of independent learners is not Markovian. Int. J. Knowledge-Based Intelligent Engineering Systems 15, 55–64 (2011)

    Article Google Scholar

  27. Foerster, J. N . et al. Stabilising experience replay for deep multi-agent reinforcement learning. In Proc. 34th Int. Conf. Mach. Learn. Vol. 70 1146–1155 (2017)

  28. Heinrich, J . & Silver, D. Deep reinforcement learning from self-play in imperfect-information games. In NIPS Deep Reinforcement Learning Workshop (2016)

  29. Jouppi, N. P . et al. In-datacenter performance analysis of a Tensor Processing Unit. Proc. 44th Annu. Int. Symp. Comp. Architecture Vol. 17 1–12 (2017)

  30. Maddison, C. J., Huang, A., Sutskever, I . & Silver, D. Move evaluation in Go using deep convolutional neural networks. In 3rd Int. Conf. Learn. Representations. (2015)

  31. Clark, C . & Storkey, A. J. Training deep convolutional neural networks to play Go. In Proc. 32nd Int. Conf. Mach. Learn. Vol. 37 1766–1774 (2015)

  32. Tian, Y. & Zhu, Y. Better computer Go player with neural network and long-term prediction. In 4th Int. Conf. Learn. Representations (2016)

  33. Cazenave, T. Residual networks for computer Go. IEEE Trans. Comput. Intell. AI Games https://doi.org/10.1109/TCIAIG.2017.2681042 (2017)

  34. Huang, A. AlphaGo master online series of games. https://deepmind.com/research/AlphaGo/match-archive/master (2017)

  35. Barto, A. G. & Duff, M. Monte Carlo matrix inversion and reinforcement learning. Adv. Neural Inf. Process. Syst. 6, 687–694 (1994)

    Google Scholar

  36. Singh, S. P. & Sutton, R. S. Reinforcement learning with replacing eligibility traces. Mach. Learn. 22, 123–158 (1996)

    MATH Google Scholar

  37. Lagoudakis, M. G. & Parr, R. Reinforcement learning as classification: leveraging modern classifiers. In Proc. 20th Int. Conf. Mach. Learn. 424–431 (2003)

  38. Scherrer, B., Ghavamzadeh, M., Gabillon, V., Lesner, B. & Geist, M. Approximate modified policy iteration and its application to the game of Tetris. J. Mach. Learn. Res. 16, 1629–1676 (2015)

    MathSciNet MATH Google Scholar

  39. Littman, M. L. Markov games as a framework for multi-agent reinforcement learning. In Proc. 11th Int. Conf. Mach. Learn. 157–163 (1994)

  40. Enzenberger, M. The integration of a priori knowledge into a Go playing neural network. http://www.cgl.ucsf.edu/go/Programs/neurogo-html/neurogo.html (1996)

  41. Enzenberger, M. in Advances in Computer Games (eds Van Den Herik, H. J., Iida, H. & Heinz, E. A. ) 97–108 (2003)

  42. Sutton, R. Learning to predict by the method of temporal differences. Mach. Learn. 3, 9–44 (1988)

    Google Scholar

  43. Schraudolph, N. N., Dayan, P. & Sejnowski, T. J. Temporal difference learning of position evaluation in the game of Go. Adv. Neural Inf. Process. Syst. 6, 817–824 (1994)

    Google Scholar

  44. Silver, D., Sutton, R. & Müller, M. Temporal-difference search in computer Go. Mach. Learn. 87, 183–219 (2012)

    Article MathSciNet Google Scholar

  45. Silver, D. Reinforcement Learning and Simulation-Based Search in Computer Go. PhD thesis, Univ. Alberta, Edmonton, Canada (2009)

  46. Gelly, S. & Silver, D. Monte-Carlo tree search and rapid action value estimation in computer Go. Artif. Intell. 175, 1856–1875 (2011)

    Article MathSciNet Google Scholar

  47. Coulom, R. Computing Elo ratings of move patterns in the game of Go. Int. Comput. Games Assoc. J. 30, 198–208 (2007)

    Google Scholar

  48. Gelly, S., Wang, Y., Munos, R. & Teytaud, O. Modification of UCT with patterns in Monte-Carlo Go. Report No. 6062 (INRIA, 2006)

  49. Baxter, J., Tridgell, A. & Weaver, L. Learning to play chess using temporal differences. Mach. Learn. 40, 243–263 (2000)

    Article Google Scholar

  50. Veness, J., Silver, D., Blair, A. & Uther, W. Bootstrapping from game tree search. In Adv. Neural Inf. Process. Syst. 1937–1945 (2009)

  51. Lai, M. Giraffe: Using Deep Reinforcement Learning to Play Chess. MSc thesis, Imperial College London (2015)

  52. Schaeffer, J., Hlynka, M . & Jussila, V. Temporal difference learning applied to a high-performance game-playing program. In Proc. 17th Int. Jt Conf. Artif. Intell. Vol. 1 529–534 (2001)

  53. Tesauro, G. TD-gammon, a self-teaching backgammon program, achieves master-level play. Neural Comput. 6, 215–219 (1994)

    Article Google Scholar

  54. Buro, M. From simple features to sophisticated evaluation functions. In Proc. 1st Int. Conf. Comput. Games 126–145 (1999)

  55. Sheppard, B. World-championship-caliber Scrabble. Artif. Intell. 134, 241–275 (2002)

    Article Google Scholar

  56. Moravcˇík, M. et al. DeepStack: expert-level artificial intelligence in heads-up no-limit poker. Science 356, 508–513 (2017)

    Article ADS MathSciNet Google Scholar

  57. Tesauro, G & Galperin, G. On-line policy improvement using Monte-Carlo search. In Adv. Neural Inf. Process. Syst. 1068–1074 (1996)

  58. Tesauro, G. Neurogammon: a neural-network backgammon program. In Proc. Int. Jt Conf. Neural Netw. Vol. 3, 33–39 (1990)

  59. Samuel, A. L. Some studies in machine learning using the game of checkers II - recent progress. IBM J. Res. Develop. 11, 601–617 (1967)

    Article Google Scholar

  60. Kober, J., Bagnell, J. A. & Peters, J. Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32, 1238–1274 (2013)

    Article Google Scholar

  61. Zhang, W. & Dietterich, T. G. A reinforcement learning approach to job-shop scheduling. In Proc. 14th Int. Jt Conf. Artif. Intell. 1114–1120 (1995)

  62. Cazenave, T., Balbo, F. & Pinson, S. Using a Monte-Carlo approach for bus regulation. In Int. IEEE Conf. Intell. Transport. Syst. 1–6 (2009)

  63. Evans, R. & Gao, J. Deepmind AI reduces Google data centre cooling bill by 40%. https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ (2016)

  64. Abe, N . et al. Empirical comparison of various reinforcement learning strategies for sequential targeted marketing. In IEEE Int. Conf. Data Mining 3–10 (2002)

  65. Silver, D., Newnham, L., Barker, D., Weller, S. & McFall, J. Concurrent reinforcement learning from customer interactions. In Proc. 30th Int. Conf. Mach. Learn. Vol. 28 924–932 (2013)

  66. Tromp, J. Tromp–Taylor rules. http://tromp.github.io/go.html (1995)

  67. Müller, M. Computer Go. Artif. Intell. 134, 145–179 (2002)

    Article Google Scholar

  68. Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016)

    Article Google Scholar

  69. Segal, R. B. On the scalability of parallel UCT. Comput. Games 6515, 36–47 (2011)

    Article MathSciNet Google Scholar

Download references

Mastering the game of Go without human knowledge (2024)
Top Articles
Latest Posts
Article information

Author: Dong Thiel

Last Updated:

Views: 6037

Rating: 4.9 / 5 (79 voted)

Reviews: 86% of readers found this page helpful

Author information

Name: Dong Thiel

Birthday: 2001-07-14

Address: 2865 Kasha Unions, West Corrinne, AK 05708-1071

Phone: +3512198379449

Job: Design Planner

Hobby: Graffiti, Foreign language learning, Gambling, Metalworking, Rowing, Sculling, Sewing

Introduction: My name is Dong Thiel, I am a brainy, happy, tasty, lively, splendid, talented, cooperative person who loves writing and wants to share my knowledge and understanding with you.