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Academic paper related to ADS-B
Sensors 2017, 17(1), 188; http://dx.doi.org/10.3390/s17010188
The full paper is available here:http://www.mdpi.com/1424-8220/17/1/188
"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"
Authors:
Santiago Álvarez de Toledo, Aurea Anguera, José M. Barreiro, Juan A. Lara* and David Lizcano
Escuela Técnica Superior de Ingenieros Informáticos, Campus de Montegancedo, Technical University of Madrid (UPM), Boadilla del Monte, 28660 Madrid, Spain
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
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
*Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 4 November 2016 / Revised: 4 January 2017 / Accepted: 11 January 2017 / Published: 19 January 2017
View Full-Text | Download PDF [7129 KB, uploaded 19 January 2017] | Browse Figures
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. View Full-Text
Keywords: machine learning; reinforcement learning; ADS-B; perception-action-value association; air navigation
The full paper is available here:http://www.mdpi.com/1424-8220/17/1/188
"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"
Authors:
Santiago Álvarez de Toledo, Aurea Anguera, José M. Barreiro, Juan A. Lara* and David Lizcano
Escuela Técnica Superior de Ingenieros Informáticos, Campus de Montegancedo, Technical University of Madrid (UPM), Boadilla del Monte, 28660 Madrid, Spain
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
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
*Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 4 November 2016 / Revised: 4 January 2017 / Accepted: 11 January 2017 / Published: 19 January 2017
View Full-Text | Download PDF [7129 KB, uploaded 19 January 2017] | Browse Figures
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. View Full-Text
Keywords: machine learning; reinforcement learning; ADS-B; perception-action-value association; air navigation