DPM: Dual Preferences-based Multi-Agent Reinforcement Learning

Sehyeok Kang, Yongsik Lee, Minu Kim, Jihwan Oh, Song Chong, Se-Young Yun

Keywords: multi-agent reinforcement learning, preference-based reinforcement learning, RLAIF, RLHF

Abstract:

Preference-based Reinforcement Learning (PbRL), which optimizes reward functions using preference feedback, is a promising approach for environments where handcrafted reward modeling is challenging. Especially in sparse-reward environments, feedback-based reward modeling achieves notable performance gains by transforming sparse feedback signals into dense ones.
However, most PbRL research has primarily focused on single-agent environments, with limited attention to multi-agent environments.
In this paper, we propose Dual Preferences-based Multi-Agent Reinforcement Learning (DPM), which extends PbRL to multi-agent tasks by introducing _dual_ preferences comparing not only whole trajectories but also individual agent contributions during transitions. Furthermore, DPM replaces human preferences with those generated by LLMs to train the reward functions. Experimental results in the StarCraft Multi-Agent Challenge (SMAC) and SMACv2 environments demonstrate significant performance improvements over baselines, indicating the efficacy of DPM in optimizing individual reward functions and enhancing performances in sparse reward settings.

Primary Area: reinforcement learning

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Submission Number: 9110