Social Laws for Multi-agent Coordination in Stochastic Environments

The paper extends the concept of social laws from deterministic, goal-based multi‑agent systems to stochastic, reward‑based environments. It introduces a formalism for defining and verifying the robustness of these laws, including a new metric called α‑robustness that quantifies the utility each agent can guarantee while following the law. The authors present a verification approach that reduces the problem to solving multiple Markov decision processes and demonstrate the framework’s potential through empirical evaluations on toy environments.

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• agents
• reinforcement-learning