Cockcroft, Matthew2023-11-102023-11-102022https://hdl.handle.net/10539/36949A dissertation submitted in fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, Johannesburg, 2022The ability to reuse skills gained from previously solved tasks is essential to building agents that can solve more complex, unseen tasks. Typically, skills are specific to the initial task for which they were learned to assist in solving and it remains a challenge to determine which features between a set of two tasks need to be similar in order for that skill to apply to both tasks. Current approaches have shown that learning generalised skill representations allows for the skills to be successfully transferred and reused in accelerating learning across multiple similar new tasks. However, these approaches require large amounts of domain knowledge and handcrafting to learn the correct representations. We propose a novel framework for autonomously learning factored skill representations, which consist only of the variables which are relevant to executing each particular skill. We show that our learned factored skills significantly outperform traditional unfactored skills, and match the performance of other methods, without requiring the prior expert knowledge that those methods do. We also display our framework’s applicability to realworld settings by demonstrating its ability to scale to a realistic simulated kitchen environment.enFactored SkillReuse skills.1 Q-LearnDissertation