Reinforcement Learning for Assignment problem
  This paper is dedicated to the application of reinforcement learning combined
with neural networks to the general formulation of user scheduling problem. Our
simulator resembles real world problems by means of stochastic changes in
environment. We applied Q-learning based method to the number of dynamic
simulations and outperformed analytical greedy-based solution in terms of total
reward, the aim of which is to get the lowest possible penalty throughout
simulation.
