Learning complex, hierarchical tasks or diverse, open-ended tasks when rewards are sparse or there is no clear success criterion remains a challenge for RL agents. Manually crafting dense shaping rewards is non-trivial and even potentially infeasible for some environments, and choosing a good heuristic requires domain knowledge, typically resulting in task- and environment-specific solutions. This project explores alternative approaches which instead leverage information-rich language feedback and other multi-modal signals resulting directly from interactions of embodied agents with their environment.
Before joining the CDT RAS, I completed an MSc in Artificial Intelligence from the University of Strathclyde, Glasgow, where I graduated with Distinction and at the top of my class. My MSc dissertation combined my interests in multi-modality, representation learning and affective computing. I previously worked as a Data Analyst and was involved in a range of Data Science projects for leading brands.