Abstract
In this paper, we present two sucessful applications of Reinforcement Learning (RL) in real life. First, the optimization of anemia management in patients undergoing Chronic Renal Failure is presented. The aim is to individualize the treatment (Erythropoietin dosages) in order to stabilize patients within a targeted range of Hemoglobin (Hb). Results show that the use of RL increases the ratio of patients within the desired range of Hb. Thus, patients’ quality of life is increased, and additionally, Health Care System reduces its expenses in anemia management. Second, RL is applied to modify a marketing campaign in order to maximize long-term profits. RL obtains an individualized policy depending on customer characteristics that increases long-term profits at the end of the campaign. Results in both problems show the robustness of the obtained policies and suggest their use in other real-life problems.
This work has been partially supported by the research projects ARVIV/2007/094, CSD2007-00018 and TIN2007-61006.
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Martín-Guerrero, J.D., Soria-Olivas, E., Martínez-Sober, M., Serrrano-López, A.J., Magdalena-Benedito, R., Gómez-Sanchis, J. (2008). Use of Reinforcement Learning in Two Real Applications. In: Girgin, S., Loth, M., Munos, R., Preux, P., Ryabko, D. (eds) Recent Advances in Reinforcement Learning. EWRL 2008. Lecture Notes in Computer Science(), vol 5323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89722-4_15
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DOI: https://doi.org/10.1007/978-3-540-89722-4_15
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