Abstract
We study a model of evolving populations of self-learning agents and analyze the interaction between learning and evolution. We consider agent-brokers that predict stock price changes and use these predictions for selecting actions. Each agent is equipped with a neural network adaptive critic design for behavioral adaptation. We discuss three cases in which either learning, or evolution, or both, are active in our model. We show that the Baldwin effect can be observed in our model, viz., originally acquired adaptive policy of best agent-brokers becomes inherited over the course of the evolution. Additionally, we analyze influence of neural network structure of adaptive critic design on learning processes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Red’ko, V.G., Mosalov, O.P., Prokhorov, D.V.: A model of Baldwin effect in populations of self-learning agents. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005, Montreal, Canada, vol. 3, pp. 1355–1360 (2005)
Red’ko, V.G., Mosalov, O.P., Prokhorov, D.V.: A model of evolution and learning. Neural Networks 18, 738–745 (2005)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Meyer, J.-A., Wilson, S.W. (eds.): From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior. The MIT Press/Bradford Books, Cambridge (1991)
Wilson, S.W.: The animat path to AI. In: [4], pp. 15–21
Baldwin, J.M.: A new factor in evolution. American Naturalist 30, 441–451 (1896)
Belew, R.K., Mitchell, M. (eds.): Adaptive Individuals in Evolving Populations: Models and Algorithms. Santa Fe Institute Studies in the Sciences of Complexity, vol. 26. Addison-Wesley, Reading (1996)
Prokhorov, D., Puskorius, G., Feldkamp, L.: Dynamical neural networks for control. In: Kolen, J., Kremer, S. (eds.) A Field Guide to Dynamical Recurrent Networks, pp. 23–78. IEEE Press, Los Alamitos (2001)
Moody, J., Wu, L., Liao, Y., Saffel, M.: Performance function and reinforcement learning for trading systems and portfolios. Journal of Forecasting 17, 441–470 (1998)
Rumelhart, D.E., Hinton, G.E., Williams, R.G.: Learning representation by back-propagating error. Nature 323, 533–536 (1986)
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975); MIT Press, Boston, MA (1992)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Red’ko, V.G., Prokhorov, D.V. (2010). Learning and Evolution of Autonomous Adaptive Agents. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-05177-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05176-0
Online ISBN: 978-3-642-05177-7
eBook Packages: EngineeringEngineering (R0)