Combining Propositional Logic Based Decision Diagrams with Decision
  Making in Urban Systems
  Solving multiagent problems can be an uphill task due to uncertainty in the
environment, partial observability, and scalability of the problem at hand.
Especially in an urban setting, there are more challenges since we also need to
maintain safety for all users while minimizing congestion of the agents as well
as their travel times. To this end, we tackle the problem of multiagent
pathfinding under uncertainty and partial observability where the agents are
tasked to move from their starting points to ending points while also
satisfying some constraints, e.g., low congestion, and model it as a multiagent
reinforcement learning problem. We compile the domain constraints using
propositional logic and integrate them with the RL algorithms to enable fast
simulation for RL.
