In this context, deep reinforcement learning has emerged as a promising approach for inverse design with desired properties that maximizes the outcome based on learning from existing knowledge. In particular, this approach has been successfully applied in de novo drug design . The approach allows learning how to design molecules that maximize a reward function using deep learning models such as recurrent neural networks and generative adversarial networks. Apart from molecule generation, Pan et al. demonstrated that the reinforcement learning approach could be extended to inorganic materials such as metal oxide. However, the complex crystal structure of MOFs, which typically contain more than 100 atoms per unit cell, presents a significant challenge for this approach. In this work, we propose a new deep reinforcement learning framework designed for large crystalline materials such as MOFs, which can tackle the complexity of crystalline systems through reinforcement learning.