The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications
This work addresses the multi-agent pickup and delivery problem for warehouse automation, but it is incremental as it focuses on benchmarking existing methods rather than introducing new solutions.
The paper benchmarks two state-of-the-art algorithms, conflict-based search (CBS) and shared experience actor-critic (SEAC), for solving the multi-agent pickup and delivery problem in a simulated warehouse environment, aiming to provide a comprehensive comparison of their performance.
We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL). Specifically, a recent MAPF algorithm called conflict-based search (CBS) and a current MARL algorithm called shared experience actor-critic (SEAC) are studied. While the performance of these algorithms is measured using quite different metrics in their separate lines of work, we aim to benchmark these two methods comprehensively in a simulated warehouse automation environment.