  We present preliminary results from our sixth placed entry to the Flatland
international competition for train rescheduling, including two improvements
for optimized reinforcement learning (RL) training efficiency, and two
hypotheses with respect to the prospect of deep RL for complex real-world
control tasks: first, that current state of the art policy gradient methods
seem inappropriate in the domain of high-consequence environments; second, that
learning explicit communication actions (an emerging machine-to-machine
language, so to speak) might offer a remedy. These hypotheses need to be
confirmed by future work. If confirmed, they hold promises with respect to
optimizing highly efficient logistics ecosystems like the Swiss Federal
Railways railway network.
