Reinforcement Learning Algorithms for Autonomous Mission Accomplishment by Unmanned Aerial Vehicles: A Comparative View with DQN, SARSA, and A2C

Jiménez, Gonzalo Aguilar and de la Escalera Hueso, Arturo and Gómez-Silva, Maria J. (2023) Reinforcement Learning Algorithms for Autonomous Mission Accomplishment by Unmanned Aerial Vehicles: A Comparative View with DQN, SARSA, and A2C. Sensors, 23 (21). p. 9013. ISSN 1424-8220

[thumbnail of sensors-23-09013.pdf] Text
sensors-23-09013.pdf - Published Version

Download (5MB)

Abstract

Unmanned aerial vehicles (UAV) can be controlled in diverse ways. One of the most common is through artificial intelligence (AI), which comprises different methods, such as reinforcement learning (RL). The article aims to provide a comparison of three RL algorithms—DQN as the benchmark, SARSA as a same-family algorithm, and A2C as a different-structure one—to address the problem of a UAV navigating from departure point A to endpoint B while avoiding obstacles and, simultaneously, using the least possible time and flying the shortest distance. Under fixed premises, this investigation provides the results of the performances obtained for this activity. A neighborhood environment was selected because it is likely one of the most common areas of use for commercial drones. Taking DQN as the benchmark and not having previous knowledge of the behavior of SARSA or A2C in the employed environment, the comparison outcomes showed that DQN was the only one achieving the target. At the same time, SARSA and A2C did not. However, a deeper analysis of the results led to the conclusion that a fine-tuning of A2C could overcome the performance of DQN under certain conditions, demonstrating a greater speed at maximum finding with a more straightforward structure.

Item Type: Article
Subjects: Eurolib Press > Multidisciplinary
Depositing User: Managing Editor
Date Deposited: 07 Nov 2023 06:23
Last Modified: 07 Nov 2023 06:23
URI: http://info.submit4journal.com/id/eprint/2960

Actions (login required)

View Item
View Item