TY - GEN
T1 - Trajectory Design for Unmanned Aerial Vehicles via Meta-Reinforcement Learning
AU - Lu, Ziyang
AU - Wang, Xueyuan
AU - Gursoy, M. Cenk
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper considers the trajectory design problem for unmanned aerial vehicles (UAVs) via meta-reinforcement learning. It is assumed that the UAV can move in different directions to explore a specific area and collect data from the ground nodes (GNs) located in the area. The goal of the UAV is to reach the destination and maximize the total data collected during the flight on the trajectory while avoiding collisions with other UAVs. In the literature on UAV trajectory designs, vanilla learning algorithms are typically used to train a task-specific model, and provide near-optimal solutions for a specific spatial distribution of the GNs. However, this approach requires retraining from scratch when the locations of the GNs vary. In this work, we propose a meta reinforcement learning framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). Instead of training task-specific models, we train a common initialization for different distributions of GNs and different channel conditions. From the initialization, only a few gradient descents are required for adapting to different tasks with different GN distributions and channel conditions. Additionally, we also explore when the proposed MAML framework is preferred and can outperform the compared algorithms.
AB - This paper considers the trajectory design problem for unmanned aerial vehicles (UAVs) via meta-reinforcement learning. It is assumed that the UAV can move in different directions to explore a specific area and collect data from the ground nodes (GNs) located in the area. The goal of the UAV is to reach the destination and maximize the total data collected during the flight on the trajectory while avoiding collisions with other UAVs. In the literature on UAV trajectory designs, vanilla learning algorithms are typically used to train a task-specific model, and provide near-optimal solutions for a specific spatial distribution of the GNs. However, this approach requires retraining from scratch when the locations of the GNs vary. In this work, we propose a meta reinforcement learning framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). Instead of training task-specific models, we train a common initialization for different distributions of GNs and different channel conditions. From the initialization, only a few gradient descents are required for adapting to different tasks with different GN distributions and channel conditions. Additionally, we also explore when the proposed MAML framework is preferred and can outperform the compared algorithms.
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U2 - 10.1109/INFOCOMWKSHPS57453.2023.10226090
DO - 10.1109/INFOCOMWKSHPS57453.2023.10226090
M3 - Conference contribution
AN - SCOPUS:85171616062
T3 - IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023
BT - IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023
Y2 - 20 May 2023
ER -