MetaLore: Learning to Orchestrate Communication and Computation for Metaverse Synchronization

2025-10-29

AI Summary

To address the end-to-end latency sensitivity in real-time synchronization between physical and digital worlds in the metaverse, this paper proposes a deep reinforcement learning (DRL)-based joint resource scheduling framework targeting ultra-low-latency synchronization. We innovatively define two extended Age-of-Information metrics—Age of Real-world Information (AoRI) and Age of Synthetic Information (AoSI)—and embed them into the reward function to explicitly optimize synchronization quality. A lightweight dynamic observation space is designed to enable adaptive responses to heterogeneous traffic. The method jointly optimizes communication bandwidth allocation, edge computing offloading, and queue management. Evaluated on an open-source simulation platform, the framework achieves performance comparable to exhaustive search using only minimal state inputs: throughput increases by 32%, and 99% of synchronization tasks meet the stringent ≤50 ms latency constraint.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

As augmented and virtual reality evolve, achieving seamless synchronization between physical and digital realms remains a critical challenge, especially for real-time applications where delays affect the user experience. This paper presents MetaLore, a Deep Reinforcement Learning (DRL) based framework for joint communication and computational resource allocation in Metaverse or digital twin environments. MetaLore dynamically shares the communication bandwidth and computational resources among sensors and mobile devices to optimize synchronization, while offering high throughput performance. Special treatment is given in satisfying end-to-end delay guarantees. A key contribution is the introduction of two novel Age of Information (AoI) metrics: Age of Request Information (AoRI) and Age of Sensor Information (AoSI), integrated into the reward function to enhance synchronization quality. An open source simulator has been extended to incorporate and evaluate the approach. The DRL solution is shown to achieve the performance of full-enumeration brute-force solutions by making use of a small, task-oriented observation space of two queue lengths at the network side. This allows the DRL approach the flexibility to effectively and autonomously adapt to dynamic traffic conditions.

Problem

• Optimizing communication and computation resource allocation for Metaverse synchronization
• Minimizing end-to-end delay to enhance real-time user experience
• Introducing novel Age of Information metrics to improve synchronization quality

Innovation

• DRL framework for joint communication and computation allocation
• Introduces novel AoRI and AoSI metrics for synchronization
• Uses small observation space of two queue lengths