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In industrial environments, an increasing amount of wireless devices areused, which utilize license-free bands. As a consequence of these mutualinterferences of wireless systems might decrease the state of coexistence.Therefore, a central coexistence management system is needed, which allocatesconflict-free resources to wireless systems. To ensure a conflict-free resourceutilization, it is useful to predict the prospective medium utilization beforeresources are allocated. This paper presents a self-learning concept, which isbased on reinforcement learning. A simulative evaluation of reinforcementlearning agents based on neural networks, called deep Q-networks and doubledeep Q-networks, was realized for exemplary and practically relevantcoexistence scenarios. The evaluation of the double deep Q-network showed thata prediction accuracy of at least 98 % can be reached in all investigatedscenarios.

Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning