Thomas M. S. Smith
PhD Researcher in Reinforcement Learning
My research focuses on how artificially intelligent agents can learn to represent the world around them as predictive knowledge, and how they can use this knowledge to better complete tasks. In particularly my research addresses methods for agents to learn which behaviours can be completed successfully in their surroundings, and to predict the consequences of these behaviours. I consider these behaviours under the framework of options and hierarchical reinforcement learning.
Currently I am considering methods for learning and applying affordances. Affordance is best captured by the question “what can I do here?”. It describes the behaviours that a particular agent is able to complete given some environment features. For example, a button affords being pressed, or a door affords being opened. These affordances can speed up learning and planning for an agent.
I am part of the Bath Reinforcement Learning Lab, supervised by Professor Özgür Şimşek.
news
Jul 21, 2023 | Attending ICML 2023 with Dan Beechey next week to present our work: Explaining Reinforcement Learning with Shapley Values |
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