MARLMUI: Multi-Agent Reinforcement Learning Approach in Mobile Adaptive User Interface

Abstract

The article is devoted to the study of machine learning approaches in the processing of the user interface of mobile ecosystems in order to test the approach of adaptive interfaces, which formalizes them as a stochastic sequential decision problem, as well as the use of multi-agent model-based reinforcement learning for adaptation planning. This article introduces for the first time the use of reinforcement learning in a mobile adaptive user interface. The article presents adaptation options based on changing the representation of elements, as well as a transition function for the model of Markov decision processes. This article proposes a novel method called MARLMUI, a Multi-Agent Reinforcement Learning Mobile User Interface. In conclusion, Dec-POMDP, a decentralized partially observable MDP model, is considered as a proposed interface processing algorithm based on multi-agent reinforcement learning. This study is first attempt to systematize knowledge and practically implement an adaptive interface in the mobile ecosystem.