We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applications of autonomous vehicles such as drone traffic management, we present zone-based path finding (or ZBPF) where agents move among zones, and agents' movements require uncertain travel time. Furthermore, each zone can accommodate multiple agents (as per its capacity). We also develop a simulator for ZBPF which provides a clean interface from the simulation environment to learning algorithms. We develop a novel formulation of the ZBPF problem using difference-of-convex functions (DC) programming. The resulting approach can be used for policy learning using samples from the simulator. We also present a multiagent credit assignment scheme that helps our learning approach converge faster. Empirical results in a number of 2D and 3D instances show that our approach can effectively minimize congestion in zones, while ensuring agents reach their final destinations.

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Sequential multiagent decision-making under partial observability and uncertainty poses several challenges. Although multiagent reinforcement learning (MARL) approaches have increased the scalability, addressing combinatorial domains is still challenging as random exploration by agents is unlikely to generate useful reward signals. We address cooperative multiagent pathfinding under uncertainty and partial observability where agents move from their respective sources to destinations while also satisfying constraints (e.g., visiting landmarks). Our main contributions include: (1) compiling domain knowledge such as underlying graph connectivity and domain constraints into propositional logic based decision diagrams, (2) developing modular techniques to integrate such knowledge with deep MARL algorithms, and (3) developing fast algorithms to query the compiled knowledge for accelerated episode simulation in RL. Empirically, our approach can tractably represent various types of domain constraints, and outperforms previous MARL approaches significantly both in terms of sample complexity and solution quality on a number of instances.

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