My current research interests lie in learning symbols for planning in continuous, low-level state spaces. In particular, I am interested in how best to learn symbolic representations of environments which can be transferred to other domains that an agent may face. This work is supervised by Benjamin Rosman of the CSIR, and George Konidaris at Brown University.
Previous work focused on developing a better understanding of Monte Carlo Tree Search and the effect of different simulation policies on the overall performance of agents in environments with varying characteristics. The results of this work can be seen on my Google Scholar profile here.