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Arxiv
3d
318
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When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
- The success of teams in robotics, nature, and society relies on diversified specialists, but a clear explanation of when diversity is better than uniformity is lacking.
- Research focuses on reward design in multi-agent task allocation problems to determine the effectiveness of heterogeneous teams.
- The study examines different types of reward objectives for heterogeneous teams in a non-spatial setting.
- They use generalized aggregation operators to determine whether heterogeneity can enhance rewards.
- It is proven that the curvature of these operators affects how heterogeneity impacts rewards, with a simple convexity test for broad reward families.
- The study investigates how heterogeneity emerges in embodied agents learning effort allocation policies.
- Multi-Agent Reinforcement Learning (MARL) and Heterogeneous Environment Design (HED) are used to optimize scenarios where diversity is beneficial.
- Experiments in matrix games and a Multi-Goal-Capture environment confirm that HED aligns with theoretical predictions regarding the advantages of heterogeneity.
- The findings contribute to understanding when behavioral diversity leads to tangible benefits in cooperative multi-agent learning.
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