MetaLore: Learning to Orchestrate Communication and Computation for Metaverse Synchronization

TL;DR

MetaLore employs deep reinforcement learning to optimize communication and computation resource sharing in the Metaverse, improving synchronization and delay guarantees through novel AoI metrics and a flexible, adaptive approach.

Contribution

It introduces MetaLore, a DRL framework with novel AoI metrics for joint resource allocation in Metaverse environments, enabling autonomous adaptation to dynamic traffic.

Findings

Achieves near-optimal performance comparable to brute-force solutions.

Effectively adapts to dynamic traffic conditions with a small observation space.

Enhances synchronization quality with new AoI 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.