Multicopy Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2309.10908v3
• Date: Fri, 16 May 2025 18:41:35 GMT
• Title: Multicopy Reinforcement Learning Agents
• Authors: Alicia P. Wolfe, Oliver Diamond, Brigitte Goeler-Slough, Remi Feuerman, Magdalena Kisielinska, Victoria Manfredi,
• Abstract summary: This paper examines a novel type of multi-agent problem, in which an agent makes multiple identical copies of itself in order to achieve a single agent task better or more efficiently.<n>We propose a learning algorithm for this multicopy problem which takes advantage of the structure of the value function to efficiently learn how to balance the advantages and costs of adding additional copies.
• Score: 0.23090185577016445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper examines a novel type of multi-agent problem, in which an agent makes multiple identical copies of itself in order to achieve a single agent task better or more efficiently. This strategy improves performance if the environment is noisy and the task is sometimes unachievable by a single agent copy. We propose a learning algorithm for this multicopy problem which takes advantage of the structure of the value function to efficiently learn how to balance the advantages and costs of adding additional copies.

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