🤖 AI Summary
This work addresses the vulnerability of cooperative resilience—the capacity of a multi-agent system to anticipate, resist, recover from, and adapt to disturbances—in mixed-motive environments to reward design. It proposes a novel framework that explicitly optimizes for cooperative resilience as a learning objective by inferring reward functions from ranked behavioral trajectories, leveraging preference-based reward learning with three parameterization strategies: linear models, handcrafted features, and neural networks. By integrating a hybrid reward scheme that combines individual task rewards with resilience-inferred rewards, the approach significantly enhances system robustness in social dilemma settings. Empirical results demonstrate that this resilience-oriented reward design maintains task performance while substantially reducing the risk of systemic collapse and resource overexploitation, thereby underscoring its critical role in fostering sustainable cooperation.
📝 Abstract
Multi-agent systems often operate in dynamic and uncertain environments, where agents must not only pursue individual goals but also safeguard collective functionality. This challenge is especially acute in mixed-motive multi-agent systems. This work focuses on cooperative resilience, the ability of agents to anticipate, resist, recover, and transform in the face of disruptions, a critical yet underexplored property in Multi-Agent Reinforcement Learning. We study how reward function design influences resilience in mixed-motive settings and introduce a novel framework that learns reward functions from ranked trajectories, guided by a cooperative resilience metric. Agents are trained in a suite of social dilemma environments using three reward strategies: i) traditional individual reward; ii) resilience-inferred reward; and iii) hybrid that balance both. We explore three reward parameterizations-linear models, hand-crafted features, and neural networks, and employ two preference-based learning algorithms to infer rewards from behavioral rankings. Our results demonstrate that hybrid strategy significantly improve robustness under disruptions without degrading task performance and reduce catastrophic outcomes like resource overuse. These findings underscore the importance of reward design in fostering resilient cooperation, and represent a step toward developing robust multi-agent systems capable of sustaining cooperation in uncertain environments.