A Multiagent Deep Reinforcement Learning Scheme for Energy Use Optimization in UAV‐Enabled Wireless Networks With Reconfigurable Intelligent Surfaces

This work introduces a multiagent deep reinforcement learning (MADRL) framework for energy harvesting (EH) in unmanned aerial vehicle (UAV) networks aided by reconfigurable intelligent surfaces (RIS). The core goal is to maximize quantities of harvested energy subject to quality of service (QoS) constraints in dynamic wireless setups. The considered model involves centralized training alongside decentralized execution, together with replay‐based learning to yield stable convergence. Extensive experiments are included in a comparison between MADRL and evolution strategies (ES), deep deterministic policy gradient (DDPG), stochastic DDPG (SD3), and state of the art twin delayed DDPG (TD3)–based approaches. The outcomes verify that MADRL ensures an average throughput of over 300 Mbps when deployed with four UAVs, exceeding DDPG and closing in on TD3 and adaptive TD3, and consumes minimal processing time and memory resources. In time‐domain tests, MADRL maintains an EH fraction in the range of approximately 0.27–0.31 ((mean≈0.29)), and in dual‐domain evaluation, it sustains an EH fraction of approximately 0.73–0.75 (mean≈0.74), indicating robust energy performance under both scenarios. Parameter sensitivity analysis also confirms the selection of hyperparameter α , β , η , and γ as optimal trade‐offs at α = 0.6, β = 0.8, η = 3 × 10 −4 , γ = 0.98. The computational tests verify potential practicability in real‐time applications, where MADRL only takes 0.42 s and 1.8 GBs, respectively, for each episode, and in terms of memory. These confirmations reflect the potential applicability of MADRL in scalable UAV RIS networks and thus provide potential applications in energy‐efficient wireless setups.