Opportunities of Reinforcement Learning in South Africa's Just Transition
It targets socio-economic and climate problems in South Africa, but is incremental as it applies existing RL methods to new domains without novel technical contributions.
This paper explores the potential of Reinforcement Learning (RL) to support South Africa's Just Transition by enhancing agriculture, energy management, and transportation, aiming to address socio-economic and climate challenges.
South Africa stands at a crucial juncture, grappling with interwoven socio-economic challenges such as poverty, inequality, unemployment, and the looming climate crisis. The government's Just Transition framework aims to enhance climate resilience, achieve net-zero greenhouse gas emissions by 2050, and promote social inclusion and poverty eradication. According to the Presidential Commission on the Fourth Industrial Revolution, artificial intelligence technologies offer significant promise in addressing these challenges. This paper explores the overlooked potential of Reinforcement Learning (RL) in supporting South Africa's Just Transition. It examines how RL can enhance agriculture and land-use practices, manage complex, decentralised energy networks, and optimise transportation and logistics, thereby playing a critical role in achieving a just and equitable transition to a low-carbon future for all South Africans. We provide a roadmap as to how other researchers in the field may be able to contribute to these pressing problems.