An end-to-end approach to a Reinforcement Learning application in the transport logistics
Ramón Gómez, Nerea (2022)
This research describes an end-to-end application of Reinforcement Learning to solve a multimodal
routing problem.
This application starts with the data collection through Blockchain in order to guarantee
the security, transparency and scalability of the databases. With a robust data we proceed
to make a fictional simulation of a routing scenario with three modes of transport.
The RL agent will have to learn how to design the routes based on a model-free RL
method. Four different agents are trained, each one of them considering a different strategy
to design the route.
After the training, an evaluation of the results will be carried out. This evaluation is
completed by comparing the results against the decisions made by a Dijkstra shortest path
algorithm. This comparison is of sufficient granularity to be able to judge the accuracy of
the RL algorithm.
The present application demonstrates the feasibility of applying this discipline to a routing
problem endowed with a high level of variability such as real life presents.
Ramón_Gómez_MA_EEMCS.pdf