  The Flatland competition aimed at finding novel approaches to solve the
vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling
trips in traffic networks and the re-scheduling of vehicles when disruptions
occur, for example the breakdown of a vehicle. While solving the VRSP in
various settings has been an active area in operations research (OR) for
decades, the ever-growing complexity of modern railway networks makes dynamic
real-time scheduling of traffic virtually impossible. Recently, multi-agent
reinforcement learning (MARL) has successfully tackled challenging tasks where
many agents need to be coordinated, such as multiplayer video games. However,
the coordination of hundreds of agents in a real-life setting like a railway
network remains challenging and the Flatland environment used for the
competition models these real-world properties in a simplified manner.
Submissions had to bring as many trains (agents) to their target stations in as
little time as possible. While the best submissions were in the OR category,
participants found many promising MARL approaches. Using both centralized and
decentralized learning based approaches, top submissions used graph
representations of the environment to construct tree-based observations.
Further, different coordination mechanisms were implemented, such as
communication and prioritization between agents. This paper presents the
competition setup, four outstanding solutions to the competition, and a
cross-comparison between them.
