  Climate Change is an incredibly complicated problem that humanity faces. When
many variables interact with each other, it can be difficult for humans to
grasp the causes and effects of the very large-scale problem of climate change.
The climate is a dynamical system, where small changes can have considerable
and unpredictable repercussions in the long term. Understanding how to nudge
this system in the right ways could help us find creative solutions to climate
change.
  In this research, we combine Deep Reinforcement Learning and a World-Earth
system model to find, and explain, creative strategies to a sustainable future.
This is an extension of the work from Strnad et al. where we extend on the
method and analysis, by taking multiple directions. We use four different
Reinforcement Learning agents varying in complexity to probe the environment in
different ways and to find various strategies. The environment is a
low-complexity World Earth system model where the goal is to reach a future
where all the energy for the economy is produced by renewables by enacting
different policies. We use a reward function based on planetary boundaries that
we modify to force the agents to find a wider range of strategies. To favour
applicability, we slightly modify the environment, by injecting noise and
making it fully observable, to understand the impacts of these factors on the
learning of the agents.
