Abstract
Wargame systems are artificial combat simulation platforms which would be practical in game research. The widely used methods in wargame systems mostly rely on refined experience of human experts. We suppose to apply artificial intelligence methods rather than expert-experience-based methods to complicated game environments. Reinforcement learning methods provide a human-like normative way which guides agents upgrade their behaviors in game environments without expert experience. This paper reveals the performances of both experience-based models and reinforcement-learning-based models in game environments. This environment presented in this paper is a type of zero-sum game which means there only be one winner. Our experiments show that reinforcement-learning-based models is more robust and powerful than expert-experience-based methods but cost more time.
The work is supported by both National scientific and Technological Innovation Zero (No. 17-H863-01-ZT-005-005-01) and State’s Key Project of Research and Development Plan (No. 2016QY03D0505). The contributions of authors of this paper are as follows: Wu proposed this problem, built computational models and do experiments; Liao, Lv, Duan and Zhao provided various supports for this work.
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References
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)
Bishop, C.M.: Neural networks for pattern recognition. Agric. Eng. Int. CIGR J. Sci. Res. Dev. Manuscript Pm 12(5), 1235–1242 (1995)
Browne, C.B., et al.: A survey of monte carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4(1), 1–43 (2012)
Campbell, M., Hoane Jr., A.J., Hsu, F.H.: Deep blue. Artif. Intell. 134(1), 57–83 (2002)
Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M.J. (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75538-8_7
Ernest, N.D.: Genetic fuzzy trees for intelligent control of unmanned combat aerial vehicles. Dissertations & Theses - Gradworks (2015)
Goldberg, D.E.: Genetic Algorithm in Search Optimization and Machine Learning, vol. xiii, no. 7, pp. 2104–2116. Addison Wesley, Boston (1989)
Guo, X., Singh, S., Lee, H., Lewis, R., Wang, X.: Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning. In: International Conference on Neural Information Processing Systems, pp. 3338–3346 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)
Kuhlmann, T., Lamping, R., Massow, C.: Intelligent decision support. J. Mater. Process. Technol. 76(13), 257–260 (1998)
Maddison, C.J., Huang, A., Sutskever, I., Silver, D.: Move evaluation in go using deep convolutional neural networks. Comput. Sci. (2015)
Miller, F.P., Vandome, A.F., Mcbrewster, J.: Atari 2600. Alphascript Publishing (2013)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)
Mnih, V., et al.: Playing Atari with deep reinforcement learning. Comput. Sci. (2013)
Passino, K.M., Yurkovich, S.: Fuzzy Control. Tsinghua University Press, Beijing (2001)
Russell, S.J., Norvig, P.: Artificial intelligence: a modern approach. Appl. Mech. Mater. 263(5), 2829–2833 (2010)
Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354–359 (2017)
Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction, bradford book. IEEE Trans. Neural Netw. 16(1), 285–286 (2005)
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Wu, W., Liao, M., Lv, P., Duan, X., Zhao, X. (2019). Performance Comparison Between Genetic Fuzzy Tree and Reinforcement Learning in Gaming Environment. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_23
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