Exploiting environmental signals to enable policy correlation in large-scale decentralized systems

نویسندگان

چکیده

Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of commons in common-pool resources, spite bleak prediction non-cooperative game theory. On top that, real-world problems are inherently large-scale low observability. One key concept that facilitates coordination such settings is use conventions. Inspired by behavior, we investigate learning dynamics emergence temporal conventions, focusing on resources. Extra emphasis was given designing a realistic evaluation setting: (a) environment modeled fisheries, (b) assume decentralized learning, where can observe only their own history, (c) run simulations (up 64 agents). Uncoupled policies observability make cooperation hard achieve; as number grow, probability taking correct gradient direction decreases exponentially. By introducing an arbitrary common signal (e.g., date, time, or any periodic set numbers) means couple process, show conventions emerge reach sustainable harvesting strategies. The introduction consistently improves social welfare (by 258% average, up 3306%), range environmental parameters sustainability be achieved 46% 300%), convergence speed abundance 13% 53%).

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ژورنال

عنوان ژورنال: Autonomous Agents and Multi-Agent Systems

سال: 2022

ISSN: ['1387-2532', '1573-7454']

DOI: https://doi.org/10.1007/s10458-021-09541-7