  This work demonstrates the potential of deep reinforcement learning
techniques for transmit power control in wireless networks. Existing techniques
typically find near-optimal power allocations by solving a challenging
optimization problem. Most of these algorithms are not scalable to large
networks in real-world scenarios because of their computational complexity and
instantaneous cross-cell channel state information (CSI) requirement. In this
paper, a distributively executed dynamic power allocation scheme is developed
based on model-free deep reinforcement learning. Each transmitter collects CSI
and quality of service (QoS) information from several neighbors and adapts its
own transmit power accordingly. The objective is to maximize a weighted
sum-rate utility function, which can be particularized to achieve maximum
sum-rate or proportionally fair scheduling. Both random variations and delays
in the CSI are inherently addressed using deep Q-learning. For a typical
network architecture, the proposed algorithm is shown to achieve near-optimal
power allocation in real time based on delayed CSI measurements available to
the agents. The proposed scheme is especially suitable for practical scenarios
where the system model is inaccurate and CSI delay is non-negligible.
