Multi-Agent Path Planning Using Deep Reinforcement Learning

In this paper a deep reinforcement based multi-agent path planning approach is introduced. The experiments are realized in a simulation environment and in this environment different multi-agent path planning problems are produced. The produced problems are actually similar to a vehicle routing problem and they are solved using multi-agent deep reinforcement learning. In the simulation environment, the model is trained on different consecutive problems in this way and, as the time passes, it is observed that the model's performance to solve a problem increases. Always the same simulation environment is used and only the location of target points for the agents to visit is changed. This contributes the model to learn its environment and the right attitude against a problem as the episodes pass. At the end, a model who has already learned a lot to solve a path planning or routing problem in this environment is obtained and this model can already find a nice and instant solution to a given unseen problem even without any training. In routing problems, standard mathematical modeling or heuristics seem to suffer from high computational time to find the solution and it is also difficult and critical to find an instant solution. In this paper a new solution method against these points is proposed and its efficiency is proven experimentally.