Reinforcement learning for licensed-assisted access of LTE in the unlicensed spectrum In order to coexist with the WiFi systems in the unlicensed spectrum, Long Term Evolution (LTE) networks can utilize periodically configured transmission gaps. In this paper, considering a time division duplex (TDD)-LTE system, we propose a Q-Learning based dynamic duty cycle selection technique for configuring LTE transmission gaps, so that a satisfactory throughput is maintained both for LTE and WiFi systems. By explicitly taking the impact of IEEE 802.11n beacon transmission mechanism into account, we evaluate the coexistence performance of WiFi and LTE using the proposed technique. Simulation results show that the proposed approach can enhance the overall capacity performance by 19% and WiFi capacity performance by 77%, hence enabling effective coexistence of LTE and WiFi systems in the unlicensed band.