Generalizing human-Earth systems modeling and decision-making: A multi-agent deep reinforcement learning framework

Earth system is characterized by intricate interactions between human activities and natural processes, where stochastic dynamics, nonlinear feedbacks, and emergent behaviors collectively determine system evolution and sustainability outcomes. Despite significant advances in Earth system science, two fundamental challenges persist: the insufficient integration of physical process models with observational data, and the lack of interpretable frameworks for simulating coupled human-Earth dynamics and optimizing governance strategies. These limitations critically impede our ability to conduct effective Earth system governance and guide human-environment interactions toward sustainable development pathways. To overcome these challenges, this study proposes an innovative framework that synergistically integrates data assimilation and reinforcement learning to enhance both predictability and decision-making capabilities in the complex Earth system. Data assimilation, as a well-established methodology in Earth system science, systematically combines dynamic models with multi-source observations to improve system observability and forecast accuracy. Reinforcement learning, grounded in the Bellman equation and Markov decision processes, provides a natural paradigm for modeling adaptive human-environment interactions and deriving optimal strategies through sequential decision-making under uncertainty. Building upon these complementary methodologies, we develop a Multi-Agent Deep Reinforcement Learning (MADRL) framework that employs the Markov decision process as the theoretical foundation, integrates agent-based modeling to represent heterogeneous stakeholder behaviors across multiple organizational levels, utilizes deep neural networks to handle high-dimensional state-action spaces, and incorporates data assimilation techniques to continuously update system states and reduce forecast uncertainties. This integrated framework is specifically designed to address fundamental Earth system governance challenges by capturing emergent phenomena arising from complex human-environment interactions, enabling the exploration of intervention mechanisms such as economic incentives, regulatory policies, and cooperative arrangements, and providing interpretable decision pathways that balance economic development with environmental sustainability. Through this integration, our framework offers a systematic approach to tackle classical problems in Earth system governance, from the tragedy of the commons to planetary boundaries, ultimately advancing our capacity to navigate toward sustainable development trajectories in an increasingly coupled human-Earth system.