We presentCentury Maze,1 a simulated environment for cooperative navigation of groups numbering over 100 robots with primarily visual observations. The underlying task of large-scale object search is designed to push the scalability and efficiency of decentralized multi-robot systems relying on communication to overcome the limitations of separate units. To enable the training of robotic agents in large-scale visual embodied applications such asCentury Maze, we propose two reinforcement learning (RL) components that facilitate the emergence of effective communication:Differentiable Inter-Agent Belief Learning (DIABL), which provides a clear learning signal by optimizing the agents' beliefs about their goals and propagating gradients back over a differentiable communication channel to include the appropriate observers, andInformative Event Replay (InfER), which compensates for the scarcity of samples relevant toDIABLby maintaining a dedicated buffer for auxiliary learning. We demonstrate our combined method through an actor-critic neural network architecture encompassing a decentralized multi-agent system of variable size with individual goal-based belief modules. Results onCentury Mazeshow that our method is able to significantly improve performance through cooperation between over 100 agents against comparable baselines and maintains its effectiveness in realistic conditions.

This paper addresses the problem of establishing efficient intralogistic systems, focusing on the generation of roadmaps on a given layout and the coordination of multiple Automated Guided Vehicles (AGVs). A simulation-based approach for automatic roadmap design is proposed. An event-based simulator is developed that uses ant-colony inspired optimization to generate roadmaps tailored to the specific characteristics of a given intralogistic problem, i.e., the plant layout, fleet size, statistical description of tasks, dispatching algorithm, etc. The generated solutions are evaluated with a Multi-Agent Path Finding (MAPF) simulator that uses a Safe Interval Path Planning (SIPP) algorithm. By analysing the system throughput, the optimal fleet size for the system is proposed. The approach is validated through various examples and benchmarked against existing methods in the literature.Note to Practitioners—Creating efficient roadmaps plays a key role in improving the efficiency of multi-AGV systems. Usually, roadmaps are designed by experts, which is time consuming and may lead to suboptimal solutions due to the complexity of the problem. The main objective of this work is to develop a methodology that allows the automatic generation of efficient roadmaps taking into account the specific characteristics of an intralogistic problem. The main advantage is the fast generation of solutions and their validation in terms of throughput. At the same time, the presented methodology allows a quick correction of the solution if one of the problem characteristics changes, without the need to change the layout itself. This provides industry practitioners with an effective tool that facilitates their work in setting up multi-AGV systems, thus saving time and costs.