mlsanityData to 14 Oct 2026

Joint Trajectory, RIS, and Computation Offloading Optimization via Decentralized Model-Based PPO in Urban Multi-UAV Mobile Edge Computing

arXiv 2603.20238 ↗First posted 9 Mar 2026eess.SY cs.IT cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Efficient computation offloading in multi-UAV edge networks becomes particularly challenging in dense urban areas, where line-of-sight (LoS) links are frequently blocked and user demand varies rapidly. Reconfigurable intelligent surfaces (RISs) can mitigate blockage by creating controllable reflected links, but realizing their potential requires tightly coupled decisions on UAV trajectories, offloading schedules, and RIS phase configurations. This joint optimization is hard to solve in practice because multiple UAVs must coordinate under limited information exchange, and purely model-free multi-agent reinforcement learning (MARL) often learns too slowly in highly dynamic environments. To address these challenges, we propose a decentralized model-based MARL framework. Each UAV optimizes mobility and offloading using observations from several hop neighbors, and submits an RIS phase proposal that is aggregated by a lightweight RIS controller. To boost sample efficiency and stability, agents learn local dynamics models and perform short horizon branched rollouts for proximal policy optimization (PPO) updates. Simulations show near centralized performance with improved throughput and energy efficiency at scale.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 39 references that are in the map. First posted on arXiv 9 Mar 2026.

Builds on

16 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning9 Feb

Other references · 15

  1. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 194
  2. 2018Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Taskscited by 121
  3. 2020Multi-agent Reinforcement Learning for Networked System Controlcited by 53
  4. 2023Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescuecited by 14
  5. 2024DiffSG: A Generative Solver for Network Optimization with Diffusion Modelcited by 14
  6. 2023Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learningcited by 10
  7. 2024Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computingcited by 10
  8. 2021Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Controlcited by 7
  9. 2022Scalable Model-based Policy Optimization for Decentralized Networked Systemscited by 7
  10. 2024GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networkscited by 7
  11. 2021Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradientscited by 5
  12. 2024Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD updatecited by 4
3 more
  1. 2022Joint Optimization of Resource Allocation, Phase Shift and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systemscited by 3
  2. 2024QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing via an Online Joint Optimization Approachcited by 2
  3. 2025Enhancing Secrecy Energy Efficiency in RIS-Aided Aerial Mobile Edge Computing Networks: A Deep Reinforcement Learning Approachcited by 1

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  1. 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach
  2. 2025Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
  3. 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL
  4. 2025AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
  5. 2024Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs
  6. 2025Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks
  7. 2025Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms
  8. 2025Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach