Abstract
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.
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Di Nocera, D., Finzi, A., Rossi, S., Staffa, M. (2012). Attentional Action Selection Using Reinforcement Learning. In: Ziemke, T., Balkenius, C., Hallam, J. (eds) From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science(), vol 7426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_37
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DOI: https://doi.org/10.1007/978-3-642-33093-3_37
Publisher Name: Springer, Berlin, Heidelberg
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