Social Laws for Multi-agent Coordination in Stochastic Environments
Researchers extend social laws to stochastic, reward-based multi-agent environments, defining alpha-robustness and a verification method via Markov decision processes.
The paper extends the concept of social laws from deterministic, goal-based settings to stochastic, reward-based multi-agent environments. It introduces alpha-robustness, a measure of the guaranteed utility each agent retains while pursuing its optimal single-agent policy assuming all agents obey the social law. Robustness verification is reduced to solving a series of Markov decision processes, with empirical evaluations on toy environments.
- Social laws formalism extended to stochastic, reward-based settings
- Alpha-robustness measures guaranteed per-agent utility under law compliance
- Verification reduced to solving a series of MDPs
From the original article
1 min read at arxiv.orgIn multi-agent environments, coordinating agents to prevent interference and ensure robust individual performance is a critical challenge. Previous research on social laws for multi-agent systems has primarily focused on deterministic, goal-based settings.
By Rolando Fernandez · arxiv.org
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