UAV Coverage Path Planning With Quantum-Based Recurrent Deep Deterministic Policy Gradient
Silvirianti Silvirianti, Bhaskara Narottama, Soo Young Shin
Abstract
This study proposes quantum-based deep deterministic policy gradient (Q-DDPG) and quantum-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recurrent</i> 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.