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UAV Coverage Path Planning With Quantum-Based Recurrent Deep Deterministic Policy Gradient
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DOI：10.1109/TVT.2023.3347219.png)
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Abstract
En 中文
This study proposes quantum-based deep deterministic policy gradient (Q-DDPG) and quantum-based recurrent DDPG (Q-RDDPG) schemes for time-series optimization in UAV communications. Herein, Q-DDPG-based actor-critic reinforcement learning is utilized to optimize action selections in a large state and continuous action space. In this scheme, quantum models are exploited to reduce computational complexity and training loss. As a particular case, Q-DDPG and Q-RDDPG are employed for trajectory optimization and dynamic resource allocation in UAV communications. The results demonstrate that Q-DDPG and Q-RDDPG schemes achieved higher rewards with lower training losses compared to classical DDPG.
Keywords:
Autonomous aerial vehicles
Training
Optimization
NOMA
Encoding
Vehicle dynamics
Resource management
Deep deterministic policy gradient
energy efficiency
quantum embedding
recurrent
UAV communications
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
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Cited Papers
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