A Reinforcement learning Approach for Climate Change Adaptation and Policy: Using Deep Q-Network for Multi-Sectorial Resilience in Kitui County, Kenya

Climate change presents serious challenges, especially in dry and semi-dry areas, and this is most severe in sub-Saharan Africa, where people are dependent on rain-fed agriculture. To address this issue, a new type of reinforcement learning (RL) mechanism that can be used to simulate and
optimize climate adaptation policies in Kitui County, Kenya, was developed. In this study, combined geospatial data encompassing several variables, such as precipitation, the health of vegetation, the number of people, and economic indicators, was used to create a custom reinforcement learning environment. A Deep Q-Network (DQN) agent was developed to distribute restricted financial resources amongst nine climate adaptation policies over a 25-year period simulation. The findings clearly show that the artificial intelligence agent targeted the long-term sustainability of the ecosystem. In connection to this, it made the choice of agroforestry in 76.0% of its decision cycles, and at the same time, it tactically bypassed high-capital, high-latency infrastructure such as dams and opted for distributed adaptive measures. This framework provides a powerful tool to support policymakers in a data-driven manner to analyze the interdependence of urgent aid and sustainable development. The study demonstrates the possibilities of using AI-powered decision support systems in the field of climate policy planning for the most affected areas.