The rapid growth of global data volumes has created a demand for scalable distributed systems that can maintain a high quality of service. Data replication is a widely used technique that provides fault tolerance, improved performance and higher availability. Traditional implementations often rely on threshold-based activation mechanisms, which can vary depending on workload changes and system architecture. System administrators typically bear the responsibility of adjusting these thresholds. To address this challenge, reinforcement learning can be used to dynamically adapt to workload changes and different architectures. In this paper, we propose a novel data replication strategy for cloud systems that employs reinforcement learning to automatically learn system characteristics and adapt to workload changes. The strategy's aim is to provide satisfactory Quality of Service while optimizing a trade-off between provider profit and environmental impact. We present the architecture behind our solution and describe the reinforcement learning model by defining the states, actions and rewards.