Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems

TL;DR

This paper introduces a method combining propositional logic decision diagrams with reinforcement learning to improve multiagent pathfinding in urban environments, addressing uncertainty, partial observability, and safety constraints.

Contribution

It presents a novel integration of propositional logic-based decision diagrams with reinforcement learning for multiagent pathfinding under complex urban constraints.

Findings

Enhanced simulation speed for RL with logic constraints

Improved safety and congestion management in multiagent systems

Effective handling of uncertainty and partial observability

Abstract

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.