Abstract
This paper thoroughly analyzes the transfer and construction of the state-action space of the agent decision-making process, discusses the optimal strategy of agent’s action selection based on Markov decision-making process, designs a neural networks model for the agent reinforcement learning, and designs the agent reinforcement learning based on neural networks. By the simulation experiment of agent’s bid price in Multi-Agent Electronic Commerce System, validated the Agent Reinforcement Learning Algorithm Based on Neural Networks has very good performance and the action impending ability.
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References
Rubo, Z., Guochang, G., et al.: Reinforcement Learning Theory,Algorithm and Its Application[J]. Control Theory and Applications 5(17), 637–641 (2000)
Burnsa, T.R., Gomolinskab, A.: Socio-cognitive mechanisms of belief change Applications of generalized game theory to belief revision, social fabrication, and self-fulfilling prophesy[J]. Journal of Cognitive Systems Research 2, 39–54 (2001)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey[J]. Journal of Artificial Intelligence Research 4, 237–285 (1996)
Tsitsiklis, J.N., Roy, B.V.: An analysis of temporal difference learning with function approximation[J]. IEEE Transactions on Automatic Control 42, 674–690 (1997)
Watkins, C.J., Dayan, P.: Q-learning[J]. Machine Learning 8, 279–292 (1992)
Singh, S.P., et al.: Convergence results for single-step on strategy reinforcement learning algorithms[J]. Machine Learning 38, 287–308 (2000)
Boutilier, C.: Planning, learning and coordination in multi-agent decision processes[J]. In: Proceedings of the Sixth Conference on Theoretical Aspects of Rationality and Knowledge[C ], pp. 195–210. Morgan Kaufmann, San Francisco (1996)
Haykin, S.: Neural Networks: A Comprehensive Foundation[M]. Prentice Hall Tsinghua University Press (2001)
Tang, L., Li, S., Cheng, D.: The Research on Multi-Agent Based Matchmaking Tradeoff Model and Algorithm. Computer Engineering and Applications(in Chinese) [J] 23(46), 145–147 (2003)
Tang, L., Cheng, D.: Structure of Multi-agent Making Match Tradeoff System. Computer Engineering(in Chinese) [J] 15(29), 166–168 (2003)
Yang, G., Shi-fu, C., et al.: Rearch on reinforcement learning technology: A Review. Acta Automatica Sinica[J] 30(1), 86–100 (2004)
Wallace, R.J., Freuder, E.C.: Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving. Artificial Intelligence[J] 161, 209–227 (2005)
Yu, Z., Guo-chang, G., et al.: Surey of distributed reinforcement learning algorithms in multi-agent systems. Control Theory & Applications[J] 20(3), 317–322 (2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction [M]. MIT Press, Cambridge (1998)
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Tang, L.G., An, B., Cheng, D.J. (2007). An Agent Reinforcement Learning Model Based on Neural Networks. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_14
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DOI: https://doi.org/10.1007/978-3-540-74769-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74768-0
Online ISBN: 978-3-540-74769-7
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