Many such developments can be envisaged, for example in the type of reinforcement learning algorithm that could be applied beyond policy gradient methods. The learning model in RL-CSP operates only with energy as a feature to define the state space. This simplicity allows efficient operation. However, enriching the feature space with relevant structural characteristics would make learning quicker and more accurate. For this purpose, one could implement known structural characteristics such as the bond valence sum, or deploy neural networks to reveal the most relevant structural features, such as characteristic local coordination geometries for specific elements. More advanced RL algorithms can be adapted to the basin-hopping routine, for example, actor-critic methods were developed to deal with the high variance often encountered in policy gradient methods . In non-basin-hopping CSP algorithms containing decisions that constitute a policy, these decisions could also be amenable to reinforcement learning optimisation.