  The problem of resource constrained scheduling in a dynamic and heterogeneous
wireless setting is considered here. In our setup, the available limited
bandwidth resources are allocated in order to serve randomly arriving service
demands, which in turn belong to different classes in terms of payload data
requirement, delay tolerance, and importance/priority. In addition to
heterogeneous traffic, another major challenge stems from random service rates
due to time-varying wireless communication channels. Various approaches for
scheduling and resource allocation can be used, ranging from simple greedy
heuristics and constrained optimization to combinatorics. Those methods are
tailored to specific network or application configuration and are usually
suboptimal. To this purpose, we resort to deep reinforcement learning (DRL) and
propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm
combined with Deep Sets to tackle the aforementioned problem. Furthermore, we
present a novel way to use a Dueling Network, which leads to further
performance improvement. Our proposed algorithm is tested on both synthetic and
real data, showing consistent gains against state-of-the-art conventional
methods from combinatorics, optimization, and scheduling metrics.
