Automating abstraction for potential-based reward shaping

Within the field of Reinforcement Learning (RL) the successful application of abstraction can play a huge role in decreasing the time required for agents to learn competent policies. Many examples of this speed-up have been observed throughout the literature. Reward Shaping is one such technique for utilising abstractions in this way. This thesis focuses on how an agent can learn its own abstractions from its own experiences to be used for Potential Based Reward Shaping. As the thesis progresses, the environments for which the abstraction construction is automated grow in complexity and scope --- while also utilising less external knowledge of the domains. This culminates in the approaches \textit{Uniform Property State Abstraction} (UPSA) and \textit{Latent Property State Abstraction} (LPSA), which can both augment existing RL algorithms and allow them to construct abstractions from their own experience and then effectively make use of these abstractions to improve convergence time. Empirical results from this thesis demonstrate that this approach can outperform existing deep RL algorithms such as Deep Q-Networks over a range of domains.