Abstract
Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocop Keep away which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.
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Youk, S.J., Lee, B.K. (2010). Multi-agent Reinforcement Learning Model for Effective Action Selection. In: Bandyopadhyay, S.K., Adi, W., Kim, Th., Xiao, Y. (eds) Information Security and Assurance. ISA 2010. Communications in Computer and Information Science, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13365-7_30
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DOI: https://doi.org/10.1007/978-3-642-13365-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13364-0
Online ISBN: 978-3-642-13365-7
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