A multi-agent system (MAS) is suitable for addressing tasks in a variety of domains without any programmed behaviors, which makes it ideal for the problems associated with the mobile robots. Reinforcement learning (RL) is a successful approach used in the MASs to acquire new behaviors; most of these select exact Q-values in small discrete state space and action space. This article presents a joint Q-function linearly fuzzified for a MAS’ continuous state space, which overcomes the dimensionality problem. Also, this article gives a proof for the convergence and existence of the solution proposed by the algorithm presented. This article also discusses the numerical simulations and experimental results that were carried out to validate the proposed algorithm.

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This study aims to develop systematic research about augmented reality (AR) problems, challenges, and benefits in the current applications of five fields of interest. Articles were selected from scientific, technical, academic, and medical databases of digital journals and open access papers about AR. Therefore, the method used to develop the investigation was PRISMA, which allowed us to observe interesting facts and coincidences about complexities and successful cases of AR implementation in the disciplines of education, marketing, medicine, entertainment, and industry. The summary provided in this study was the result of the exploration of 60 recent articles found and selected by relevance using the PRISMA method. The main objective of this paper is to orient and update researchers regarding current applications, benefits, challenges, and problems in AR implementation for future studies and developments.

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