  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.
