nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Motivated Reinforcement Learning Using Self-Developed Knowledge in Autonomous Cognitive Agent

verfasst von : Piotr Papiez, Adrian Horzyk

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper describes the development of a cognitive agent using motivated reinforcement learning. The conducted research was based on the example of a virtual robot, that placed in an unknown maze, was learned to reach a given goal optimally. The robot should expand knowledge about the surroundings and learn how to move in it to achieve a given target. The built-in motivation factors allow it to focus initially on collecting experiences instead of reaching the goal. In this way, the robot gradually broadens its knowledge with the advancement of exploration of its surroundings. The correctly formed knowledge is used for effective controlling the reinforcement learning routine to reach the target by the robot. In such a way, the motivation factors allow the robot to adapt and control its motivated reinforcement learning routine automatically and autonomously.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Deep Q-Network Using Reward Distribution

Nächstes Kapitel Company Bankruptcy Prediction with Neural Networks

Duch, W.: Brain-inspired conscious computing architecture. J. Mind Behav. 26, 1–22 (2005)

Horzyk, A., Starzyk, J.A., Graham, J.: Integration of semantic and episodic memories. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 3084–3095 (2017)MathSciNetCrossRef

Horzyk, A.: How does generalization and creativity come into being in neural associative systems and how does it form human-like knowledge? Neurocomputing 144, 238–257 (2014)CrossRef

Horzyk, A.: Artificial Associative Systems and Associative Artificial Intelligence, pp. 1–276. EXIT, Warsaw (2013)

Horzyk, A.: Human-like knowledge engineering, generalization, and creativity in artificial neural associative systems. In: Skulimowski, A.M.J., Kacprzyk, J. (eds.) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. AISC, vol. 364, pp. 39–51. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-19090-7_4CrossRef

Kalat, J.W.: Biological Grounds of Psychology. PWN, Warsaw (2006)

Larose, D.T.: Discovering Knowledge from Data: Introduction to Data Mining. PWN, Warsaw (2006)MATH

Rutkowski, L.: Techniques and Methods of Artificial Intelligence. PWN, Warsaw (2012)

Tadeusiewicz, R.Y.: New trends in neurocybernetics. Comput. Methods Mater. Sci. 10(1), 1–7 (2010)

10.

Smart, W.D.: Making reinforcement learning work on real robots. Ph.D. dissertation, Department of Computer Science, Brown University, Providence, RI (2002)

11.

Papiez, P.: Use of a neural network to move a robot to the designated target position in a maze using reinforcement learning. Engineering dissertation, AGH University of Science and Technology in Krakow, Supervised and Promoted by A. Horzyk (2018)

12.

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

13.

Starzyk, J.A.: Motivated learning for computational intelligence. In: Igelnik, B. (ed.) Computational Modeling and Simulation of Intellect: Current State and Future Perspectives (Chap. 11), pp. 265–292. IGI Publishing, Hershey (2011)CrossRef

14.

Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

15.

Janowski, M.: Use of a neural network to move a robot back to the starting position in a maze using supervised learning. Engineering dissertation, AGH University of Science and Technology in Krakow, Supervised and Promoted by A. Horzyk (2018)

16.

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonglou, I., Wierstra, D., Riedmiller, M.: Playing Atari with Deep Reinforcement Learning. arXiv preprint arXiv:1312.5602 (2013)

17.

Starzyk, J.A., Graham, J., Horzyk, A.: Trust in motivated learning agents. In: 2016 IEEE SSCI, pp. 1–8. Inc. 57 Morehouse Lane Red Hook, NY, 12571, USA. IEEE Xplore (2016). https://doi.org/10.1109/SSCI.2016.7850027, ISBN 978-1-5090-4239-5

18.

Tadeusiewicz, R.: Introduction to intelligent systems. In: Wilamowski, B.M., Irvin, J.D. (eds.) The Industrial Electronics Handbook Intelligent Systems, pp. 1–12. CRC Press, Boca Raton (2011)

19.

Matiisen, T.: Demystifying Deep Reinforcement Learning (2015). http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning

20.

Tadeusiewicz, R., Horzyk, A.: Man-machine interaction improvement by means of automatic human personality identification. In: Saeed, K., Snášel, V. (eds.) CISIM 2014. LNCS, vol. 8838, pp. 278–289. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45237-0_27CrossRef

Titel: Motivated Reinforcement Learning Using Self-Developed Knowledge in Autonomous Cognitive Agent
verfasst von: Piotr Papiez
Adrian Horzyk
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-91253-0_17

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"