# Deep reinforcement learning in World-Earth system models to discover sustainable management strategies

arXiv: https://arxiv.org/abs/1908.05567 · HTML: https://mlsanity.com/paper/1908.05567/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [Transformers in Reinforcement Learning: A Survey](https://mlsanity.com/paper/2307.05979/) (2023)
- [Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations](https://mlsanity.com/paper/2410.07287/) (2024)
- [Modeling the effects of environmental and perceptual uncertainty using deterministic reinforcement learning dynamics with partial observability](https://mlsanity.com/paper/2109.07259/) (2021)
- [Power and accountability in reinforcement learning applications to environmental policy](https://mlsanity.com/paper/2205.10911/) (2022)
- [Climate Change Policy Exploration using Reinforcement Learning](https://mlsanity.com/paper/2211.17013/) (2022)
- [Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models](https://mlsanity.com/paper/2312.06527/) (2023)
- [Artificial Utopia: Simulation and Intelligent Agents for a Democratised Future](https://mlsanity.com/paper/2503.07364/) (2025)
- [Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM](https://mlsanity.com/paper/2510.07971/) (2025)

## Abstract (arXiv)

Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their interactions. Identifying pathways towards a sustainable future in these models for informing policy makers and the wider public, e.g. pathways leading to a robust mitigation of dangerous anthropogenic climate change, is a challenging and widely investigated task in the field of climate research and broader Earth system science. This problem is particularly difficult when constraints on avoiding transgressions of planetary boundaries and social foundations need to be taken into account. In this work, we propose to combine recently developed machine learning techniques, namely deep reinforcement learning (DRL), with classical analysis of trajectories in the World-Earth system. Based on the concept of the agent-environment interface, we develop an agent that is generally able to act and learn in variable manageable environment models of the Earth system. We demonstrate the potential of our framework by applying DRL algorithms to two stylized World-Earth system models. Conceptually, we explore thereby the feasibility of finding novel global governance policies leading into a safe and just operating space constrained by certain planetary and socio-economic boundaries. The artificially intelligent agent learns that the timing of a specific mix of taxing carbon emissions and subsidies on renewables is of crucial relevance for finding World-Earth system trajectories that are sustainable on the long term.