# User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

arXiv: https://arxiv.org/abs/2302.01471 · HTML: https://mlsanity.com/paper/2302.01471/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Is Q-learning Provably Efficient?](https://mlsanity.com/paper/1807.03765/) (2018)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [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)
- [Hybrid Reward Architecture for Reinforcement Learning](https://mlsanity.com/paper/1706.04208/) (2017)
- [Deep Learning for Video Game Playing](https://mlsanity.com/paper/1708.07902/) (2017)
- [When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network](https://mlsanity.com/paper/2009.10601/) (2020)
- [Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training](https://mlsanity.com/paper/2107.14316/) (2021)
- [Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2206.12030/) (2022)
- [Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning](https://mlsanity.com/paper/1801.02077/) (2018)
- [Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control](https://mlsanity.com/paper/2101.08391/) (2021)
- [Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2209.13425/) (2022)
- [Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services](https://mlsanity.com/paper/2208.00369/) (2022)
- [Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning](https://mlsanity.com/paper/2107.01001/) (2021)

## Built on by (most cited first, 1 of 1)

- [Statistical QoS Provision in Business-Centric Networks](https://mlsanity.com/paper/2408.15609/) (2024)

## Abstract (arXiv)

The Metaverse is emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the Metaverse to offer a highly immersive user experience. As mobility is emphasized in the Metaverse context, VR devices reduce their weights at the sacrifice of local computation abilities. In this paper, for a system consisting of a Metaverse server and multiple VR users, we consider two cases of (i) the server generating frames and transmitting them to users, and (ii) users generating frames locally and thus consuming device energy. Moreover, in our multi-user VR scenario for the Metaverse, users have different characteristics and demands for Frames Per Second (FPS). Then the channel access arrangement (including the decisions on frame generation location), and transmission powers for the downlink communications from the server to the users are jointly optimized to improve the utilities of users. This joint optimization is addressed by deep reinforcement learning (DRL) with heterogeneous actions. Our proposed user-centric DRL algorithm is called User-centric Critic with Heterogenous Actors (UCHA). Extensive experiments demonstrate that our UCHA algorithm leads to remarkable results under various requirements and constraints.