Model-Based Decentralized Joint Optimization of Resource Allocation, Phase Shift, and UAV Trajectory for Energy-Efficient RIS Assisted UAV-MEC Systems

Mobile Edge Computing (MEC) enables low-latency, energy-efficient offloading in next-generation wireless networks, but its performance degrades when direct links are blocked in dense urban areas. To address this, recent studies integrate Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RIS) to enhance coverage and efficiency through UAV mobility and RIS-assisted virtual line-of-sight paths. However, existing centralized optimization approaches suffer from high complexity and communication overhead, making real-time coordination difficult in dynamic multi-UAV settings. To overcome these challenges, we propose a decentralized, model-based reinforcement learning (RL) framework for UAV-RIS-MEC systems, where each UAV learns trajectory, RIS configuration, task offloading, and resource allocation policies using local observations and limited$\kappa$-hop neighbor communication. By employing short-horizon branched rollouts with Proximal Policy Optimization (PPO), the proposed method achieves stable and sample-efficient learning with theoretical convergence guarantees. Simulation results show notable gains in throughput and energy efficiency over centralized and conventional decentralized baselines, demonstrating the effectiveness and scalability of the proposed approach for future MEC deployments.