论文标题
在缓解气候变化中，多机构强化学习和人类社会因素
Multi-Agent Reinforcement Learning and Human Social Factors in Climate Change Mitigation
论文作者
论文摘要
许多复杂的现实世界问题，例如缓解气候变化，与人类社会因素交织在一起。缓解气候变化是由于人类行为的固有复杂性而困难的社会困境，在全球范围内产生了影响。我们建议在这种环境中应用多机构增强学习（MARL），以开发智能代理，这些智能代理可以影响缓解气候变化时的社会因素。以这种方式部署MARL时，必须解决道德，实用和技术挑战。在本文中，我们提出了这些挑战，并概述了解决这些挑战的方法。了解如何使用智能代理人来影响人类社会因素，这对于防止其虐待是重要的，并且可以使我们对整个复杂问题的了解有益。我们提出的挑战不仅限于我们的特定应用，而且适用于更广泛的MARL。因此，为缓解气候变化的社会因素开发MAL有助于解决阻碍MARL对其他现实世界问题的适用性的一般问题，同时还激励人们讨论MARL部署的社会含义。
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.
