dc.contributor.author | Álvarez de Toledo, Santiago | |
dc.contributor.author | Anguera, Aurea | |
dc.contributor.author | Barreiro, José M. | |
dc.contributor.author | Lara Torralbo, Juan Alfonso | |
dc.contributor.author | Lizcano, David | |
dc.date.accessioned | 2018-04-20T12:16:52Z | |
dc.date.available | 2018-04-20T12:16:52Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12226/22 | |
dc.description.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. | es |
dc.language.iso | en | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.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 | es |
dc.type | article | es |
dc.description.course | 2017-18 | es |
dc.identifier.doi | 10.3390/s17010188 | |
dc.issue.number | 1 | es |
dc.journal.title | Sensors | es |
dc.page.initial | 188 | es |
dc.publisher.faculty | Escuela de Ciencias Técnicas e Ingeniería | es |
dc.rights.accessRights | openAccess | es |
dc.subject.keyword | Machine learning | es |
dc.subject.keyword | Reinforcement learning | es |
dc.subject.keyword | ADS-B | es |
dc.subject.keyword | perception-action-value association | es |
dc.subject.keyword | Air navigation | es |
dc.volume.number | 17 | es |