Abstract
When agent chooses some action and does state transition in present state in reinforcement learning, it is important subject to decide how will reward for conduct that agent chooses. In this paper, we suggest multi colony interaction ant reinforcement learning model using TD-error to original Ant-Q learning. This method is a hybrid of multi colony interaction by elite strategy and reinforcement learning applying TD-error to Ant-Q. We could know through an experiment that proposed reinforcement learning method converges faster to optimal solution than original ACS and Ant-Q.
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Lee, S., Chung, T. (2005). A Reinforcement Learning Algorithm Using Temporal Difference Error in Ant Model. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_27
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DOI: https://doi.org/10.1007/11494669_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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