  We present a crop simulation environment with an OpenAI Gym interface, and
apply modern deep reinforcement learning (DRL) algorithms to optimize yield. We
empirically show that DRL algorithms may be useful in discovering new policies
and approaches to help optimize crop yield, while simultaneously minimizing
constraining factors such as water and fertilizer usage. We propose that this
hybrid plant modeling and data-driven approach for discovering new strategies
to optimize crop yield may help address upcoming global food demands due to
population expansion and climate change.
