## 2 Related Work
Coordination is achieved by trading off the utilities and the cost by reducing the utilities depending on the number of agents already going to a specific zone. [PERSON] et al. extend the approach presented in [24] by distributing the computation and by using a more sophisticated notion of expected information gain by considering current map knowledge and the individual agent capabilities. [PERSON] et al. consider coordinating a team of mobile agents visiting several targets in a partially unknown area [25]. Their approach is based on combinatorial auctions, where agents bid on target bundles instead of single targets, as is often the case in auction strategies for exploring unknown environments. The idea is to consider synergies among targets to optimize exploration. [PERSON] et al. study the influence of communication, learning, and the number of agents in the task of mapping the target locations in a dynamic environment [26]. The research is based on a theoretical framework based on probabilistic modeling and analyzed via simulation and physical implementation. The results of multiple experiments are compared with those anticipated by the probabilistic model, and they agree that the probabilistic model is a good approximation of a multi-agent system. These results show an effective approach for learning target locations that often change. Cooperative multi-agent systems are those in which multiple agents interact jointly to solve tasks or optimize utility [27]. For example, in [28] [PERSON] et al. researched on a distributed cooperative strategy using pedestrian behavior. As a result of interactions among agents, the complexity of multi-agent problems can increase rapidly with the number of agents or their behavioral sophistication. Recently, some authors have worked on new strategies to apply frontier-based exploration methods. For example, a frontier-based exploration concept and a multi-agent flood algorithm have been used to obtain a better exploration in an unknown area [29]. In the case of [30], [PERSON] et al. have worked on combining frontier-based exploration concepts and map-building using semantic information. They propose a semantic frontier classification and selection considering a cost-utility function. Here, the semantic information is used to classify the frontier as a free area or transit area considering the map geometric characteristics. On the other hand, [PERSON] et al. work on a frontier-based approach in which robots share their local frontier points instead of sharing the whole grip map to save communication bandwidth [31]. In this work, the robots perform dynamic cooperation in which they have some specific information about their mates, such as positions. Some authors have worked on efficiency improvement of frontier-based exploration methods. For example, [PERSON] and [PERSON] combine a frontier point evaluation with random frontier points optimization (RFPO) and SLAM algorithms. The idea is to work with a multistep exploration, where the algorithms do not plan a global path from the current position, but instead establish a local exploration path size. Once an agent reaches the local exploration path size, the current optimal frontier point is reselected to avoid the robot from making repetitive decisions [32]. Other authors have focused on the integration of deep reinforcement learning techniques with existing frontier integration methods [33; 34; 35; 36; 37]. Some authors, such as [PERSON], have proposed a deep learning-based decision algorithm that uses a neural network for learning [34]. In [35], [PERSON] et al. propose a deep learning approach for multi-agent exploration. In that work, they use agent multi-channel maps, known regions, unexplored areas, and obstacles as inputs of a convolutional neural network. Other works such as [PERSON], [PERSON] et al. [36] propose an approach that incorporates a technique called dynamic Voronoi partitions. This method reduces duplicated exploration areas by assigning different targets location to individual robots. In [37] [PERSON] et al.