Desertification Control Strategies: A Hybrid Approach Using Cellular Automata and Reinforcement Learning

This paper presents a novel hybrid approach for desertification control that leverages the strengths of cellular automata (CA) modeling and reinforcement learning (RL). We employ the DESERTICAS software, a specifically designed CA model for simulating desertification dynamics. The model incorporates a variety of factors influencing land degradation, including those from the MEDALUS model, fundamental desertification properties, land-use practices, exploitability, and management. Our key contribution is to introduce a control parameter within the DESERTICAS framework. This allows us to formulate desertification control as an input-output problem and apply control theory principles to CA models. By manipulating the average intensity of a dominant factor (identified as management in this study), we can indirectly influence all other factors and potentially decelerate or even halt land degradation processes. Furthermore, we integrate a Reinforcement Learning (RL) agent into the simulation environment. This virtual entity continuously explores different management strategies, dynamically adjusting its actions based on the observed outcomes. This combination of CA modeling and RL constitutes a hybrid approach to desertification control. The experimental results show promising outcomes, with the inclusion of the RL agent leading to a significant reduction in desertified regions. This study paves the way for further exploration of hybrid CA-RL techniques for environmental applications.