title: A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology.
abstract: Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency.
authors.affiliation: Escuela Técnica Superior de Ingenieros Informáticos, Campus de Montegancedo, Technical University of Madrid (UPM), Boadilla del Monte, 28660 Madrid, Spain. santiagoalvarezdetoledo@hotmail.com.
authors.forename: Santiago
authors.initials: S
authors.lastname: Álvarez de Toledo
authors.affiliation: Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Technical University of Madrid (UPM), C/Alan Turing s/n (Ctra. de Valencia km. 7), 28031 Madrid, Spain. aanguera@eui.upm.es.
authors.forename: Aurea
authors.initials: A
authors.lastname: Anguera
authors.affiliation: Escuela Técnica Superior de Ingenieros Informáticos, Campus de Montegancedo, Technical University of Madrid (UPM), Boadilla del Monte, 28660 Madrid, Spain. jmbarreiro@fi.upm.es.
authors.forename: José M
authors.initials: JM
authors.lastname: Barreiro
authors.affiliation: Escuela de Ciencias Técnicas e Ingeniería, Madrid Open University (MOU), Crta. de la Coruña km. 38.500, Vía de Servicio, 15, Collado Villalba, 28400 Madrid, Spain. juanalfonso.lara@udima.es.
authors.forename: Juan A
authors.initials: JA
authors.lastname: Lara
authors.affiliation: Escuela de Ciencias Técnicas e Ingeniería, Madrid Open University (MOU), Crta. de la Coruña km. 38.500, Vía de Servicio, 15, Collado Villalba, 28400 Madrid, Spain. david.lizcano@udima.es.
authors.forename: David
authors.initials: D
authors.lastname: Lizcano
keywords: ADS-B; air navigation; machine learning; perception-action-value association; reinforcement learning