Abstract
Swarm intelligence has brought a new paradise for function optimization, structural optimization, multi-agent systems and other study fields. In our previous work, we proposed a neuro-fuzzy system using reinforcement learning algorithm (actor-critic method with TD error learning algorithm) to acquire optimized swarm behaviors. This paper improves the conventional learning system, which only deals with discrete state space and action space, to solve how a swarm to learn and obtain its adaptive behaviors in the continuous state space. The improved system adopts a new policy function of action which is possible to yield continuous actions corresponding to continuous states. The effectiveness of proposed system is investigated by computer simulations with more kinds of environments for the goal-exploration problem.
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Kuremoto, T., Obayashi, M., Kobayashi, K., Adachi, H., Yoneda, K. (2008). A Neuro-fuzzy Learning System for Adaptive Swarm Behaviors Dealing with Continuous State Space. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_81
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DOI: https://doi.org/10.1007/978-3-540-85984-0_81
Publisher Name: Springer, Berlin, Heidelberg
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