Modern logistic solutions encompass the use of mobile robot systems. To achieve
a successful implementation of such a system, one must consider an efficient design
of transportation task assignment system. Main responsibility of a task assignment
system is to allocate tasks in such manner that as many tasks get completed in a
given time frame. This problem is recognized as a multicriteria optimization problem.
The purpose of this thesis is to develop a task assignment algorithm that is based on
reinforcement learning. The proposed algorithm was developed using ROS platform.
We developed an algorithm with an emphasis on fast learning. The proposed algorithm
was tested in a simulated environment. It was tested alongside simple task assignment
rules that meet only single criterion of an assignment problem. Every task assignment
algorithm was tested in an hour long experiment. Robots managed to complete the
highest number of tasks in the case of the developed solution. Measurements of traveled
distances and task completion times confirmed the one-sided decisions of simple rules,
and the multicriteria decision-making process of the developed algorithm.