A joint optimization framework using SAC-based deep reinforcement learning and alternating optimization maximizes user uplink rates while keeping over-the-air computation MSE below a threshold in a UAV network.
Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://xiewenwen22.github.io/GAI-enhanced-DRL.
citation-role summary
background 1
citation-polarity summary
fields
eess.SP 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
UAV-Assisted Integrated Communication and Over-the-Air Computation with Interference Awareness
A joint optimization framework using SAC-based deep reinforcement learning and alternating optimization maximizes user uplink rates while keeping over-the-air computation MSE below a threshold in a UAV network.