Considering the specific practical case of calculating the convex hull for a phase field to identify candidate new compositions for synthesis, the ability of reinforcement learning to improve a fixed policy enables the development of policies tailored to each composition within the phase field and should favour the more rapid construction of the hull. Although it is possible that a single fixed policy performs best at the some of the compositions studied, this study indicates that this is not likely to be the case in general, favouring further development of RL in CSP. Specifically, autonomous exploration across compositions of a phase field can benefit from RL, as our work suggests at least 32% improvement of RL-CSP when compared with the starting uniform policy.