New metrics analyze reward attribution effects in multi-agent RL

A new research paper introduces EffRank/$n$ and $D_ ext{act}$, two metrics designed to analyze how reward attribution affects learned representations in cooperative multi-agent reinforcement learning (MARL) systems. The study tested these metrics on MAPPO agents within the SMACv2 protoss_5_vs_5 environment. Findings indicate that while individual rewards lead to more distinct agent roles and behaviors, the geometric properties of learned representations are primarily influenced by the observation of unit types rather than the reward structure itself. AI

IMPACT Introduces new diagnostic tools for understanding agent behavior and representation geometry in multi-agent reinforcement learning systems.

RANK_REASON The cluster contains a single academic paper detailing new metrics for MARL research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• $D_\text{act}$
• EffRank/$n$
• MAPPO
• protoss_5_vs_5
• SMACv2

• paper
• other

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