Reinforcement Learning for Assignment problem
This work addresses scheduling optimization for users in dynamic environments, but it appears incremental as it applies existing RL methods to a specific problem.
The paper tackled the user scheduling problem by applying a Q-learning-based reinforcement learning method with neural networks, outperforming an analytical greedy-based solution in terms of total reward to minimize penalties.
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.