  Deep Reinforcement Learning (DRL) techniques have been successfully applied
for solving complex decision-making and control tasks in multiple fields
including robotics, autonomous driving, healthcare and natural language
processing. The ability of DRL agents to learn from experience and utilize
real-time data for making decisions makes it an ideal candidate for dealing
with the complexities associated with the problem of workflow scheduling in
highly dynamic cloud and edge computing environments. Despite the benefits of
DRL, there are multiple challenges associated with the application of DRL
techniques including multi-objectivity, curse of dimensionality, partial
observability and multi-agent coordination. In this paper, we comprehensively
analyze the challenges and opportunities associated with the design and
implementation of DRL oriented solutions for workflow scheduling in cloud and
edge computing environments. Based on the identified characteristics, we
propose a taxonomy of workflow scheduling with DRL. We map reviewed works with
respect to the taxonomy to identify their strengths and weaknesses. Based on
taxonomy driven analysis, we propose novel future research directions for the
field.
