Agriculture plays a vital role in the global economy, providing the necessary food and resources for human survival. With the world’s population projected to surge, the demand for food is set to escalate in the coming decades. This increasing demand, coupled with the challenges posed by climate change and the detrimental effects of pollution due to fertiliz-ers, underscores the urgency for more efficient and sustainable crop management strategies. Effective crop management is a complex and time-consuming task that involves various fac-tors, including climate conditions and soil quality. Traditional crop management strategies often rely on expert knowledge to guide the decision-making process, which may be sub-optimal and prone to error. Reinforcement learning (RL) has gained significant attention in recent years as a promising approach for decision-making and control in agriculture, aiding in the management process. RL environments such as CyclesGym [51], accommodate the design of agents that oper-ate within an agricultural system, often surpassing the performance of traditional strategies. However, the optimal policy may vary heavily depending on the specific field location, due to its specific weather conditions and soil quality. In this thesis, we aim to investigate the use of RL for managing fields in multiple locations with the aim of reducing training time and data and increasing robustness compared to independent training. To this end, we plan to use multi-task learning methods and optimizers to reduce total training time, to improve RL agents’ adaptability to changing environments, and to reduce data usage required for maximum performance across multiple agricultural fields.