  We propose a learning-based framework for efficient power allocation in ad
hoc interference networks under episodic constraints. The problem of optimal
power allocation -- for maximizing a given network utility metric -- under
instantaneous constraints has recently gained significant popularity. Several
learnable algorithms have been proposed to obtain fast, effective, and
near-optimal performance. However, a more realistic scenario arises when the
utility metric has to be optimized for an entire episode under time-coupled
constraints. In this case, the instantaneous power needs to be regulated so
that the given utility can be optimized over an entire sequence of wireless
network realizations while satisfying the constraint at all times. Solving each
instance independently will be myopic as the long-term constraint cannot
modulate such a solution. Instead, we frame this as a constrained and
sequential decision-making problem, and employ an actor-critic algorithm to
obtain the constraint-aware power allocation at each step. We present
experimental analyses to illustrate the effectiveness of our method in terms of
superior episodic network-utility performance and its efficiency in terms of
time and computational complexity.
