  Assigning resources in business processes execution is a repetitive task that
can be effectively automated. However, different automation methods may give
varying results that may not be optimal. Proper resource allocation is crucial
as it may lead to significant cost reductions or increased effectiveness that
results in increased revenues.
  In this work, we first propose a novel representation that allows modeling of
a multi-process environment with different process-based rewards. These
processes can share resources that differ in their eligibility. Then, we use
double deep reinforcement learning to look for optimal resource allocation
policy. We compare those results with two popular strategies that are widely
used in the industry. Learning optimal policy through reinforcement learning
requires frequent interactions with the environment, so we also designed and
developed a simulation engine that can mimic real-world processes.
  The results obtained are promising. Deep reinforcement learning based
resource allocation achieved significantly better results compared to two
commonly used techniques.
