  In this paper, we evaluate the use of Reinforcement Learning (RL) to solve a
classic combinatorial optimization problem: the Capacitated Vehicle Routing
Problem (CVRP). We formalize this problem in the RL framework and compare two
of the most promising RL approaches with traditional solving techniques on a
set of benchmark instances. We measure the different approaches with the
quality of the solution returned and the time required to return it. We found
that despite not returning the best solution, the RL approach has many
advantages over traditional solvers. First, the versatility of the framework
allows the resolution of more complex combinatorial problems. Moreover, instead
of trying to solve a specific instance of the problem, the RL algorithm learns
the skills required to solve the problem. The trained policy can then quasi
instantly provide a solution to an unseen problem without having to solve it
from scratch. Finally, the use of trained models makes the RL solver by far the
fastest, and therefore make this approach more suited for commercial use where
the user experience is paramount. Techniques like Knowledge Transfer can also
be used to improve the training efficiency of the algorithm and help solve
bigger and more complex problems.
