The primary goal of a strong democracy should be to most accurately represent its electorate, and the way they are divided into electoral districts can drastically affect this. As a result, many methods have been proposed to algorithmically generate fairer boundaries, the majority of which focus on eliminating bias through qualitative measures, however, these often fail to produce truly fair results. This paper, therefore, aims to demonstrate how fairness can and should become a higher priority within our electoral systems through the development, implementation and application of a new reinforcement learning-based method for algorithmic redistricting that directly optimises for fairness. Specifically, the model has been applied to the parliamentary system of the UK, filling a significant gap within the literature, meaning the paper also outlines a new metric for measuring fairness in parliamentary systems that directly rewards proportionality, the seats-votes difference. The algorithm has then been evaluated on the current parliamentary constituency boundaries in the UK and was ultimately found to fulfil all initial goals as the algorithm was able to improve the map’s fairness in all experiments performed. The paper subsequently concludes with some of the limitations of the model and the seats-votes difference and ways the redistricting algorithm could be further expanded in the future.