Abstract
This paper discusses a behavior of agents under multi-agent environment in game theory. Then we assume the behavior takes probabilistic Nash equilibrium in reinforcement learning. It is well-known that the behavior provides us with poor properties. For instance, no Nash equilibrium correspond to Pareto optimal and we can’t guarantee the convergence of learning. There, it is difficult to develop a multi-agent system to proceed cooperative work with agents. This paper takes the other approach to employee mixed Nash strategy based on correlated technique in terms of Local Effect Functions, and the model is useful to achieve cooperation among agents and they are designed to assess the convergence in learning through experiments in practice.
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Igoshi, K., Miura, T., Shioya, I. (2010). Multi-agent System Environment Based on Repeated Local Effect Functions. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_35
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DOI: https://doi.org/10.1007/978-3-642-14306-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14305-2
Online ISBN: 978-3-642-14306-9
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