Title: Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks,
Abstract: This work demonstrates the potential of deep reinforcement learning
techniques for transmit power control in emerging and future wireless networks.
Various techniques have been proposed in the literature to find near-optimal
power allocations, often 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 model-free distributively
executed dynamic power allocation scheme is developed based on 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 (with weights that are changing over time). 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. This work indicates that deep reinforcement learning
based radio resource management can be very fast and deliver highly competitive
performance, especially in practical scenarios where the system model is
inaccurate and CSI delay is non-negligible.