Reinforcement learning is typically used to model and optimize action selection strategies, in this work we deploy it to optimize attentional allocation strategies while action selection is obtained as a side effect. We present a reinforcement learning approach to attentional allocation and action selection in a behavior-based robotic systems. We detail our attentional allocation mechanisms describing the reinforcement learning problem and analysing its performance in a survival domain. © 2012 Springer-Verlag.
CITATION STYLE
Di Nocera, D., Finzi, A., Rossi, S., & Staffa, M. (2012). Attentional action selection using reinforcement learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7426 LNAI, pp. 371–380). https://doi.org/10.1007/978-3-642-33093-3_37
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