  Optimizing radio transmission power and user data rates in wireless systems
via power control requires an accurate and instantaneous knowledge of the
system model. While this problem has been extensively studied in the
literature, an efficient solution approaching optimality with the limited
information available in practical systems is still lacking. This paper
presents a reinforcement learning framework for power control and rate
adaptation in the downlink of a radio access network that closes this gap. We
present a comprehensive design of the learning framework that includes the
characterization of the system state, the design of a general reward function,
and the method to learn the control policy. System level simulations show that
our design can quickly learn a power control policy that brings significant
energy savings and fairness across users in the system.
