  Recent techniques in dynamical scheduling and resource management have found
applications in warehouse environments due to their ability to organize and
prioritize tasks in a higher temporal resolution. The rise of deep
reinforcement learning, as a learning paradigm, has enabled decentralized agent
populations to discover complex coordination strategies. However, training
multiple agents simultaneously introduce many obstacles in training as
observation and action spaces become exponentially large. In our work, we
experimentally quantify how various aspects of the warehouse environment (e.g.,
floor plan complexity, information about agents' live location, level of task
parallelizability) affect performance and execution priority. To achieve
efficiency, we propose a compact representation of the state and action space
for location-aware multi-agent systems, wherein each agent has knowledge of
only self and task coordinates, hence only partial observability of the
underlying Markov Decision Process. Finally, we show how agents trained in
certain environments maintain performance in completely unseen settings and
also correlate performance degradation with floor plan geometry.
