Towards Streamlined Learning and Search for Multi-Agent Optimization

Abstract

Many real-world problems can be modeled as cooperative multi-agent systems (MAS), such as fleet management, industrial operations, and communication networks, where multiple agents collaborate to optimize a shared objective. Optimizing cooperative MAS is difficult due to the combinatorial nature of joint actions and environmental factors. Thus, many practical multi-agent optimization approaches specialize in particular problem classes to exploit structural properties for effective and efficient optimization. Unfortunately, such specializations can lead to complex and inflexible methods that cannot be seamlessly combined or transferred to novel domains without substantial engineering effort. In this paper, we advocate an approach towards streamlined learning and search for multi-agent optimization. Focusing on multi-agent path finding as an exemplary problem, we propose to simplify two popular approaches to MAPF, namely multi-agent reinforcement learning and adaptive search. Through these simplifications, we aim to enable seamless combination and transferability of our methods without substantial engineering.