# Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach

arXiv: https://arxiv.org/abs/2401.09680 · HTML: https://mlsanity.com/paper/2401.09680/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [Adversarially Trained Actor Critic for Offline Reinforcement Learning](https://mlsanity.com/paper/2202.02446/) (2022)
- [All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda](https://mlsanity.com/paper/2110.05352/) (2021)
- [Non-isotropy Regularization for Proxy-based Deep Metric Learning](https://mlsanity.com/paper/2203.08547/) (2022)
- [First-order Policy Optimization for Robust Markov Decision Process](https://mlsanity.com/paper/2209.10579/) (2022)
- [PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning](https://mlsanity.com/paper/2001.05012/) (2020)
- [CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2102.07507/) (2021)
- [Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services](https://mlsanity.com/paper/2208.05438/) (2022)
- [Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness](https://mlsanity.com/paper/2307.15975/) (2023)
- [Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality](https://mlsanity.com/paper/2111.03776/) (2021)
- [Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach](https://mlsanity.com/paper/2111.10548/) (2021)
- [FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing](https://mlsanity.com/paper/2110.12876/) (2021)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction](https://mlsanity.com/paper/2306.14683/) (2023)
- [Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses](https://mlsanity.com/paper/2309.00477/) (2023)
- [Blockchain-assisted Twin Migration for Vehicular Metaverses: A Game Theory Approach](https://mlsanity.com/paper/2309.02173/) (2023)
- [Tiny Machine Learning for Concept Drift](https://mlsanity.com/paper/2107.14759/) (2021)
- [Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality](https://mlsanity.com/paper/2305.08569/) (2023)
- [Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2309.04703/) (2023)
- [Deep Learning for Radio Resource Allocation in Multi-Cell Networks](https://mlsanity.com/paper/1808.00667/) (2018)
- [Joint User Association and Resource Pricing for Metaverse: Distributed and Centralized Approaches](https://mlsanity.com/paper/2208.06770/) (2022)
- [Learning-based Incentive Mechanism for Task Freshness-aware Vehicular Twin Migration](https://mlsanity.com/paper/2309.04929/) (2023)
- [Metaverse: Requirements, Architecture, Standards, Status, Challenges, and Perspectives](https://mlsanity.com/paper/2302.01125/) (2023)
- [Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach](https://mlsanity.com/paper/2308.04914/) (2023)

## Built on by (most cited first, 4 of 4)

- [Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2408.01173/) (2024)
- [Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks](https://mlsanity.com/paper/2410.01176/) (2024)
- [Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks](https://mlsanity.com/paper/2505.06378/) (2025)
- [Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents](https://mlsanity.com/paper/2511.22076/) (2025)

## Abstract (arXiv)

The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physical and virtual spaces, transforming drone interaction and virtual exploration. UAV Twins (UTs), as the digital twins of UAVs that revolutionize UAV applications by making them more immersive, realistic, and informative, are deployed and updated on ground base stations, e.g., RoadSide Units (RSUs), to offer metaverse services for UAV Metaverse Users (UMUs). Due to the dynamic mobility of UAVs and limited communication coverages of RSUs, it is essential to perform real-time UT migration to ensure seamless immersive experiences for UMUs. However, selecting appropriate RSUs and optimizing the required bandwidth is challenging for achieving reliable and efficient UT migration. To address the challenges, we propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses. Specifically, we formulate a multi-leader multi-follower Stackelberg model considering a new immersion metric of UMUs in the utilities of UAVs. Then, we design a Tiny Multi-Agent Deep Reinforcement Learning (Tiny MADRL) algorithm to obtain the tiny networks representing the optimal game solution. Specifically, the actor-critic network leverages the pruning techniques to reduce the number of network parameters and achieve model size and computation reduction, allowing for efficient implementation of Tiny MADRL. Numerical results demonstrate that our proposed schemes have better performance than traditional schemes.