Abstract
In this article we present EANT, a method that creates neural networks (NNs) by evolutionary reinforcement learning. The structure of NNs is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES. EANT can create NNs that are very specialised; they achieve a very good performance while being relatively small. This can be seen in experiments where our method competes with a different one, called NEAT, to create networks that control a robot in a visual servoing scenario.
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References
Hornik, K., Stinchcombe, M.B., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)
Bellman, R.E.: Adaptive Control Processes. Princeton University Press, Princeton, USA (1961)
Rojas, R.: Neural Networks - A Systematic Introduction. Springer, Berlin, Germany (1996)
Kassahun, Y., Sommer, G.: Efficient reinforcement learning through evolutionary acquisition of neural topologies. In: Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005), Bruges, Belgium, pp. 259–266 (2005)
Siebel, N.T., Kassahun, Y.: Learning neural networks for visual servoing using evolutionary methods. In: Proceedings of the 6th International Conference on Hybrid Intelligent Systems (HIS 2006), Auckland, New Zealand, 6 (4 pages) (2006)
Eiben, Á.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin, Germany (2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge, USA (1998)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)
Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks 8(3), 694–713 (1997)
Angeline, P.J., Saunders, G.M., Pollack, J.B.: An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks 5, 54–65 (1994)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2), 99–127 (2002)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Weiss, L.E., Sanderson, A.C., Neuman, C.P.: Dynamic sensor-based control of robots with visual feedback. IEEE Journal of Robotics and Automation 3(5), 404–417 (1987)
Hutchinson, S., Hager, G., Corke, P.: A tutorial on visual servo control. Tutorial notes, Yale University, New Haven, USA (1996)
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Siebel, N.T., Krause, J., Sommer, G. (2007). Efficient Learning of Neural Networks with Evolutionary Algorithms. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_47
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DOI: https://doi.org/10.1007/978-3-540-74936-3_47
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