  Governments around the world aspire to ground decision-making on evidence.
Many of the foundations of policy making - e.g. sensing patterns that relate to
societal needs, developing evidence-based programs, forecasting potential
outcomes of policy changes, and monitoring effectiveness of policy programs -
have the potential to benefit from the use of large-scale datasets or
simulations together with intelligent algorithms. These could, if designed and
deployed in a way that is well grounded on scientific evidence, enable a more
comprehensive, faster, and rigorous approach to policy making. Integrated
Assessment Models (IAM) is a broad umbrella covering scientific models that
attempt to link main features of society and economy with the biosphere into
one modelling framework. At present, these systems are probed by policy makers
and advisory groups in a hypothesis-driven manner. In this paper, we
empirically demonstrate that modern Reinforcement Learning can be used to probe
IAMs and explore the space of solutions in a more principled manner. While the
implication of our results are modest since the environment is simplistic, we
believe that this is a stepping stone towards more ambitious use cases, which
could allow for effective exploration of policies and understanding of their
consequences and limitations.
