Reinforcement learning is an area of machine learning that trains an agent to choose from a selection of low-level actions in appropriate environments. Hierarchical reinforcement learning is an extension of this, such that sequential groups of low-level actions form a higher-level action in a similar way to individual muscle fibres in your arm contracting to extend your elbow. The process of option discovery determines which sequences of low-level actions are abstracted to form a high-level action. My research will focus on efficient and dynamic methods of option discovery, that allows agents to perform well in a multitude of domains through learned, high-level actions.
BSc Computer Science, University of Bath.
My Dissertation involved the comparison of reinforcement learning algorithms in the domain of Super Mario.
Dr Özgür Şimşek