  The transition to sustainable energy is a key challenge of our time,
requiring modifications in the entire pipeline of energy production, storage,
transmission, and consumption. At every stage, new sequential decision-making
challenges emerge, ranging from the operation of wind farms to the management
of electrical grids or the scheduling of electric vehicle charging stations.
All such problems are well suited for reinforcement learning, the branch of
machine learning that learns behavior from data. Therefore, numerous studies
have explored the use of reinforcement learning for sustainable energy. This
paper surveys this literature with the intention of bridging both the
underlying research communities: energy and machine learning. After a brief
introduction of both fields, we systematically list relevant sustainability
challenges, how they can be modeled as a reinforcement learning problem, and
what solution approaches currently exist in the literature. Afterwards, we zoom
out and identify overarching reinforcement learning themes that appear
throughout sustainability, such as multi-agent, offline, and safe reinforcement
learning. Lastly, we also cover standardization of environments, which will be
crucial for connecting both research fields, and highlight potential directions
for future work. In summary, this survey provides an extensive overview of
reinforcement learning methods for sustainable energy, which may play a vital
role in the energy transition.
