# Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction

arXiv: https://arxiv.org/abs/2306.14683 · HTML: https://mlsanity.com/paper/2306.14683/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks](https://mlsanity.com/paper/1807.09995/) (2018)
- [When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds](https://mlsanity.com/paper/2208.13501/) (2022)
- [Realizing the Metaverse with Edge Intelligence: A Match Made in Heaven](https://mlsanity.com/paper/2201.01634/) (2022)
- [Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI](https://mlsanity.com/paper/2206.07384/) (2022)
- [6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions](https://mlsanity.com/paper/2204.06192/) (2022)
- [Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks](https://mlsanity.com/paper/1812.07499/) (2018)
- [Joint User Association and Resource Pricing for Metaverse: Distributed and Centralized Approaches](https://mlsanity.com/paper/2208.06770/) (2022)
- [PRIMAL: PRofIt Maximization Avatar pLacement for Mobile Edge Computing](https://mlsanity.com/paper/1510.04340/) (2015)
- [Green Energy Aware Avatar Migration Strategy in Green Cloudlet Networks](https://mlsanity.com/paper/1509.03603/) (2015)
- [Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications](https://mlsanity.com/paper/2109.08711/) (2021)
- [Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing](https://mlsanity.com/paper/2211.06681/) (2022)

## Built on by (most cited first, 10 of 10)

- [Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2407.11036/) (2024)
- [Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach](https://mlsanity.com/paper/2406.05418/) (2024)
- [Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach](https://mlsanity.com/paper/2401.09680/) (2024)
- [Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse](https://mlsanity.com/paper/2403.15285/) (2024)
- [Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses](https://mlsanity.com/paper/2406.05422/) (2024)
- [Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses](https://mlsanity.com/paper/2505.12710/) (2025)
- [Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks](https://mlsanity.com/paper/2410.01176/) (2024)
- [MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services](https://mlsanity.com/paper/2410.19665/) (2024)
- [A Multi-Agent DRL-Based Framework for Optimal Resource Allocation and Twin Migration in the Multi-Tier Vehicular Metaverse](https://mlsanity.com/paper/2502.19004/) (2025)
- [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)

## Abstract (arXiv)

Avatars, as promising digital assistants in Vehicular Metaverses, can enable drivers and passengers to immerse in 3D virtual spaces, serving as a practical emerging example of Artificial Intelligence of Things (AIoT) in intelligent vehicular environments. The immersive experience is achieved through seamless human-avatar interaction, e.g., augmented reality navigation, which requires intensive resources that are inefficient and impractical to process on intelligent vehicles locally. Fortunately, offloading avatar tasks to RoadSide Units (RSUs) or cloud servers for remote execution can effectively reduce resource consumption. However, the high mobility of vehicles, the dynamic workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making avatar migration decisions. To address these challenges, in this paper, we propose a dynamic migration framework for avatar tasks based on real-time trajectory prediction and Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs.Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem. To tackle this problem efficiently, the problem is transformed into a Partially Observable Markov Decision Process (POMDP) and solved by multiple DRL agents with hybrid continuous and discrete actions in decentralized. Numerical results demonstrate that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction and 30% with prediction and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse (AeVeM).