MOSMAC: A Multi-agent Reinforcement Learning Benchmark on Sequential Multi-Objective Tasks

Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Abstract: Recent advancements in multi-agent reinforcement learning (MARL) have demonstrated success on various cooperative multi-agent tasks. However, current benchmarks often fall short of representing realistic scenarios that demand agents to execute sequential tasks over long temporal horizons while balancing multiple objectives. To address this limitation, we introduce multi-objective SMAC (MOSMAC), a comprehensive MARL benchmark designed to evaluate MARL methods on tasks involving multiple objectives, sequential subtask assignments, and varying temporal horizons. MOSMAC requires agents to tackle a series of interconnected subtasks in StarCraft II while simultaneously optimizing for multiple objectives, including combat, safety, and navigation. Through rigorous evaluation of nine state-of-the-art MARL algorithms, we demonstrate that MOSMAC presents substantial challenges to existing methods, particularly in long-horizon scenarios. Our analysis establishes MOSMAC as an essential benchmark for bridging the gap between single-objective MARL and multi-objective MARL (MOMARL). The codes for MOSMAC are available at:https://github.com/smu-ncc/mosmac.

External IDs:dblp:conf/ifaamas/GengPST25