1 research outputs found
Multi-Agent Reinforcement Learning and Human Social Factors in Climate Change Mitigation
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.Comment: Accepted paper at COMARL AAAI 202