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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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
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