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The Metaverse is a new paradigm that aims to create a virtual environmentconsisting of numerous worlds, each of which will offer a different set ofservices. To deal with such a dynamic and complex scenario, considering thestringent quality of service requirements aimed at the 6th generation ofcommunication systems (6G), one potential approach is to adopt self-sustainingstrategies, which can be realized by employing Adaptive Artificial Intelligence(Adaptive AI) where models are continually re-trained with new data andconditions. One aspect of self-sustainability is the management of multipleaccess to the frequency spectrum. Although several innovative methods have beenproposed to address this challenge, mostly using Deep Reinforcement Learning(DRL), the problem of adapting agents to a non-stationary environment has notyet been precisely addressed. This paper fills in the gap in the currentliterature by investigating the problem of multiple access in multi-channelenvironments to maximize the throughput of the intelligent agent when thenumber of active User Equipments (UEs) may fluctuate over time. To solve theproblem, a Double Deep Q-Learning (DDQL) technique empowered by ContinualLearning (CL) is proposed to overcome the non-stationary situation, while theenvironment is unknown. Numerical simulations demonstrate that, compared toother well-known methods, the CL-DDQL algorithm achieves significantly higherthroughputs with a considerably shorter convergence time in highly dynamicscenarios.

Self-Sustaining Multiple Access with Continual Deep Reinforcement Learning for Dynamic Metaverse Applications