Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2024

Abstract

Current MARL benchmarks fall short in simulating realistic scenarios, particularly those involving long action sequences with sequential tasks and multiple conflicting objectives. Addressing this gap, we introduce Multi-Objective SMAC (MOSMAC), a novel MARL benchmark tailored to assess MARL methods on tasks with varying time horizons and multiple objectives. Each MOSMAC task contains one or multiple sequential subtasks. Agents are required to simultaneously balance between two objectives - combat and navigation - to successfully complete each subtask. Our evaluation of nine state-of-the-art MARL algorithms reveals that MOSMAC presents substantial challenges to many state-of-the-art MARL methods and effectively fills a critical gap in existing benchmarks for both single-objective and multi-objective MARL research.

Keywords

Multi-agent reinforcement learning; Multi-objective multi-agent reinforcement learning; Benchmark

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2024) : Auckland, New Zealand, May 6-10

First Page

2279

Last Page

2281

ISBN

9798400704864

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

City or Country

Auckland, New Zealand

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