A3C for drone autonomous driving using Airsim

• David Villota ^[1] ; Montserrat Gil-Martínez ^[1] ; Javier Rico Azagra ^[1]
  1. [1] Universidad de La Rioja

     Universidad de La Rioja

     Logroño, España

     
• Localización: XLII Jornadas de Automática: libro de actas, Castellón, 1 a 3 de septiembre de 2021, 2021, ISBN 978-84-9749-804-3, págs. 203-209
• Idioma: inglés
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• Resumen

  • 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.