Innate-Values-driven Reinforcement Learning for Cooperative Multi-Agent Systems

• URL: http://arxiv.org/abs/2401.05572v1
• Date: Wed, 10 Jan 2024 22:51:10 GMT
• Title: Innate-Values-driven Reinforcement Learning for Cooperative Multi-Agent Systems
• Authors: Qin Yang
• Abstract summary: Innate values describe agents' intrinsic motivations, which reflect their inherent interests and preferences to pursue goals. The essence of reinforcement learning (RL) is learning from interaction based on reward-driven (such as utilities) behaviors. This paper proposes a hierarchical compound value reinforcement learning model -- intrinsic-driven reinforcement learning -- to describe the complex behaviors of multi-agent interaction.
• Score: 1.8220718426493654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Innate values describe agents' intrinsic motivations, which reflect their inherent interests and preferences to pursue goals and drive them to develop diverse skills satisfying their various needs. The essence of reinforcement learning (RL) is learning from interaction based on reward-driven (such as utilities) behaviors, much like natural agents. It is an excellent model to describe the innate-values-driven (IV) behaviors of AI agents. Especially in multi-agent systems (MAS), building the awareness of AI agents to balance the group utilities and system costs and satisfy group members' needs in their cooperation is a crucial problem for individuals learning to support their community and integrate human society in the long term. This paper proposes a hierarchical compound intrinsic value reinforcement learning model -- innate-values-driven reinforcement learning termed IVRL to describe the complex behaviors of multi-agent interaction in their cooperation. We implement the IVRL architecture in the StarCraft Multi-Agent Challenge (SMAC) environment and compare the cooperative performance within three characteristics of innate value agents (Coward, Neutral, and Reckless) through three benchmark multi-agent RL algorithms: QMIX, IQL, and QTRAN. The results demonstrate that by organizing individual various needs rationally, the group can achieve better performance with lower costs effectively.

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