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

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.

References in corpus (7)

• Learning Combinatorial Optimization Algorithms over Graphs
• Counterfactual Multi-Agent Policy Gradients
• PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning
• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
• Neural Combinatorial Optimization with Reinforcement Learning
• Learning to Cooperate via Policy Search
• Multi-Agent Path Finding with Delay Probabilities