开源强化学习工具箱，助力材料循环与碳中和目标实现

The demand of finite raw materials will keep increasing as they fuel modern society. Simultaneously, solutions for stopping carbon emissions in the short term are not available, thus making the net zero target extremely challenging to achieve at scale. The circular economy (CE) paradigm is gaining attention as a solution to address climate change and the uncertainties of supplies of critical materials. Hence, in this paper, we introduce CiRL, a deep reinforcement learning (DRL) library of environments focused on the circularity control of both solid and fluid materials. The integration of DRL into the design of material circularity is possible thanks to the formalism of thermodynamical material networks, which is underpinned by compartmental dynamical thermodynamics. Along with the focus on circularity, this library has three more features: the new CE-oriented environments are in the state-space form, which is typically used in dynamical systems analysis and control design; it is based on a state-of-the-art Python library of DRL algorithms, namely, Stable-Baselines3; and it is developed in Google Colaboratory to be accessible to researchers from different disciplines and backgrounds as is often the case for circular economy researchers and engineers. CiRL is intended to be a tool to generate AI-driven actions for optimizing the circularity of supply-recovery chains and to be combined with human-driven decisions derived from material flow analysis (MFA) studies. CiRL is publicly available.