  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.
