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This paper introduces an entropy-augmented policy evaluation strategy to enhance multi-objective evolutionary algorithms in multiagent systems, specifically addressing the limitations of NSGA-II in promoting behavioral diversity. By incorporating an entropy bonus into agent fitness scores, the method discourages homogeneity and encourages exploration of distinct behavioral policies. The approach was validated through rover-domain experiments, achieving hypervolume improvements of up to 48% compared to the NSGA-II baseline, highlighting the significance of behavioral diversity in optimizing multi-objective outcomes.
Behavioral diversity boosts multiagent performance, yielding up to 48% better outcomes than traditional optimization methods.
Autonomous agent teams deployed in settings such as marine and extraterrestrial outposts must coordinate actions to achieve optimal outcomes across multiple competing objectives. Multi-objective evolutionary algorithms such as NSGA-II optimize for diversity in the objective space, but neglect diversity in the behavior space, possibly leading to premature convergence and a collapse in behaviors that may differentiate policies in different external conditions. To address this, we introduce an entropy-augmented policy evaluation strategy that incorporates an entropy bonus into agent fitness scores, discouraging behavioral homogeneity across the evolving population. By augmenting policy evaluation with a behavior-space diversity signal while preserving the underlying Pareto optimization framework, our method is designed to encourage exploration of behaviorally distinct policies in multiagent domains. We evaluate our approach across rover-domain experiments with qualitatively distinct reward structures and observe hypervolume improvements of up to 48% relative to the NSGA-II baseline, suggesting that behavioral diversity is a promising and underexplored direction for improving multi-objective multiagent evolutionary optimization.
