Asynchronous Hybrid Reinforcement Learning for Latency and Reliability
Optimization in the Metaverse over Wireless Communications
- URL: http://arxiv.org/abs/2212.14749v1
- Date: Fri, 30 Dec 2022 14:40:00 GMT
- Title: Asynchronous Hybrid Reinforcement Learning for Latency and Reliability
Optimization in the Metaverse over Wireless Communications
- Authors: Wenhan Yu, Terence Jie Chua, Jun Zhao
- Abstract summary: Real-time digital twinning of real-world scenes is increasing.
The disparity in transmitted scene dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL)
We design a novel multi-agent reinforcement learning algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC)
- Score: 8.513938423514636
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Technology advancements in wireless communications and high-performance
Extended Reality (XR) have empowered the developments of the Metaverse. The
demand for Metaverse applications and hence, real-time digital twinning of
real-world scenes is increasing. Nevertheless, the replication of 2D physical
world images into 3D virtual world scenes is computationally intensive and
requires computation offloading. The disparity in transmitted scene dimension
(2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and
downlink (DL). To ensure the reliability and low latency of the system, we
consider an asynchronous joint UL-DL scenario where in the UL stage, the
smaller data size of the physical world scenes captured by multiple extended
reality users (XUs) will be uploaded to the Metaverse Console (MC) to be
construed and rendered. In the DL stage, the larger-size 3D virtual world
scenes need to be transmitted back to the XUs. The decisions pertaining to
computation offloading and channel assignment are optimized in the UL stage,
and the MC will optimize power allocation for users assigned with a channel in
the UL transmission stage. Some problems arise therefrom: (i) interactive
multi-process chain, specifically Asynchronous Markov Decision Process (AMDP),
(ii) joint optimization in multiple processes, and (iii) high-dimensional
objective functions, or hybrid reward scenarios. To ensure the reliability and
low latency of the system, we design a novel multi-agent reinforcement learning
algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC). Extensive
experiments demonstrate that compared to proposed baselines, AAHC obtains
better solutions with preferable training time.
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