Abstract
This paper describes architecture of an artificial intelligence system based on the Elman neural network. Simple training algorithms and neural network models are not able to solve such a complex problem as movements in the conditions of an independent game world environment, so a combination of a base neural network training algorithm and Q-learning agent approach is used as part of a player behavior control model. The paper also includes results of experiments with different values of model and game world characteristics and shows efficiency of the described approach.
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References
Hornik, K., Stinchcombe, M., White, H.: Multilayer feed forward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Chen, T.P., Chen, H.: Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems. IEEE Trans. Neural Netw. 6(4), 911–917 (1995)
Leshno, M., Lin, V.Y., Pinkus, A., Schocken, S.: Multilayer feed forward networks with a nonpolynomial activation function can approximate any function. Neural Netw. 6(6), 861–867 (1993)
Wilamowski, B.M.: Neural networks and fuzzy systems for nonlinear applications. In: Proceedings of 11th INES 2007–11th International Conference Intelligent Engineering Systems, Budapest, Hungary, June 29–July 1 2007, pp. 13–19 (2007)
Macal, C.M., North, M.J.: Tutorial on agent-based modeling and simulation part 2: how to model with agents. In: Proceedings of Winter Simulation Conference WSC 2006, 3–6 December 2006, pp. 73–83 (2006)
Riedmiller, M., Braun, H.: A direct adaptive method for faster back propagation learning: the RPROP algorithm. In: Proceedings of International Conference Neural Networks, San Francisco, CA, pp. 586–591 (1993)
Demystifying Deep Reinforcement Learning. http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/
Q-Learning. http://mnemstudio.org/path-finding-q-learning-tutorial.htm
Neural Networks for Machine Learning. https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
Stroustrup, B.: Programming: Principles and Practice Using C++, 2nd edn. Addison-Wesley, Upper Saddle River (2014)
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Kuznetsov, D., Plotnikova, N. (2019). Aspects of Using Elman Neural Network for Controlling Game Object Movements in Simplified Game World. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_38
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DOI: https://doi.org/10.1007/978-3-319-91189-2_38
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