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Multi-Agent Reinforcement Learning and Human Social Factors in Climate Change Mitigation

by   Kyle Tilbury, et al.
University of Waterloo

Many complex real-world problems, such as climate change mitigation, are intertwined with human social factors. Climate change mitigation, a social dilemma made difficult by the inherent complexities of human behavior, has an impact at a global scale. We propose applying multi-agent reinforcement learning (MARL) in this setting to develop intelligent agents that can influence the social factors at play in climate change mitigation. There are ethical, practical, and technical challenges that must be addressed when deploying MARL in this way. In this paper, we present these challenges and outline an approach to address them. Understanding how intelligent agents can be used to impact human social factors is important to prevent their abuse and can be beneficial in furthering our knowledge of these complex problems as a whole. The challenges we present are not limited to our specific application but are applicable to broader MARL. Thus, developing MARL for social factors in climate change mitigation helps address general problems hindering MARL's applicability to other real-world problems while also motivating discussion on the social implications of MARL deployment.


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