Reinforcement Learning for Assignment problem

2020-11-08 · Filipp Skomorokhov, George Ovchinnikov

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.

📄 PDF Abstract BibTeX arXiv:2011.03909

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Q-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Scheduling

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Dynamic task assignment concerns the optimal assignment of resources to tasks in a business process. Recently, Deep Reinforcement Learning (DRL) has been proposed as the state of the art for solving assignment problems. …

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