Villota Miranda, DavidGil-Martínez, M.Rico-Azagra, Javier2021-08-242021-08-242021Villota, D., Gil,. M., Rico, J. A3C for drone autonomous driving using Airsim. En XLII Jornadas de Automática: libro de actas. Castelló, 1-3 de septiembre de 2021 (pp. 203-209). DOI capítulo: https://doi.org/10.17979/spudc.9788497498043.203 DOI libro: https://doi.org/10.17979/spudc.9788497498043978-84-9749-804-3http://hdl.handle.net/2183/28307[Abstract] In this work, we apply artificial intelligence to guide a drone to a certain point autonomously. Unreal engine creates a virtual environment where the drone can fly, and the algorithm is trained simulating the drone dynamics thanks to Airsim plugin. The implemented algorithm is Asynchronous Actor-Critic Advantage (A3C), which trains a neural network with less computing resources than standard reinforcement learning algorithms that normally needs costly GPUs. To prove these advantages, several experiments are run using a different number of parallel simulations (threads). The drone should reach a point randomly generated each episode. The reward, the value and the advantage function are used to evaluate the performance. As expected, these experiments show that a higher number of threads helps the leaning process improve and become more stable. These learning results are of interest to optimize the computing resources in future applications.engAtribución-NoComercial-CompartirIgual 4.0 Internacional https://creativecommons.org/licenses/by-nc-sa/4.0/deed.eshttp://creativecommons.org/licenses/by-nc-sa/3.0/es/A3CActor criticReinforcement learningAutonomous drivingAirsimMultithreadconference outputopen accesshttps://doi.org/10.17979/spudc.9788497498043.203