Environmental-Impact-Based Multi-Agent Reinforcement Learning

• Published in: Applied sciences Vol. 14; no. 15; p. 6432
• Main Authors: Alamiyan-Harandi, Farinaz, Ramazi, Pouria
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Autonomous vehicles; Behavior; Comparative analysis; Cooperation; deep reinforcement learning; Employee motivation; Environmental impact; inequity aversion model; multi-agent systems; Social aspects; Social responsibility
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14156432

To promote cooperation and strengthen the individual impact on the collective outcome in social dilemmas, we propose the Environmental-impact Multi-Agent Reinforcement Learning (EMuReL) method where each agent estimates the “environmental impact” of every other agent, that is, the difference in the current environment state compared to the hypothetical environment in the absence of that other agent. Inspired by the inequity aversion model, the agent then compares its own reward with that of its fellows multiplied by their environmental impacts. If its reward exceeds the scaled reward of one of its fellows, the agent takes “social responsibility” toward that fellow by reducing its own reward. Therefore, the less influential an agent is in reaching the current state, the more social responsibility is taken by other agents. Experiments in the Cleanup (resp. Harvest) test environment demonstrated that agents trained based on EMuReL learned to cooperate more effectively and obtained 54% (39%) and 20% (44%) more total rewards while preserving the same cooperation levels compared to when they were trained based on the two state-of-the-art reward reshaping methods: inequity aversion and social influence.