Interactive machine learning describes a collection of methodologies in which a human user actively participates in a novice agent’s learning process, through providing corrective or evaluate feedback or demonstrative actions. A primary assumption in these methods is that user input is at worst nearoptimal, however a realistic set of demonstrations will often contain conflicting or poor examples, which degrade the quality of the learnt policies. This project explores methods for the detection of such undesirable features in data and develops an algorithm for policy training with suboptimal demonstrations, while leveraging the generalisation and scalability qualities of artificial neural networks. Uncertainty estimation, which presents a structured approach for the quantification of a network’s confidence in the accuracy of its output, based on the observed training data, is applied for the detection of unwanted features in a demonstration dataset. The particular focus of this project is conflicting data resulting from scenarios with equivalent action choices, such as the obstacle avoidance setting. Following thorough testing on various environments, it is shown that novice policies may be trained to achieve a desired goal in multi-dimensional spaces with either discrete or continuous data, despite the presence of conflicts in this training data.