Biography
I am a DPhil student supervised by Prof. Michael A. Osborne and Prof. Yee Whye Teh. I am a member of the MLRG and OxCSML groups. My research interests span deep reinforcement learning, meta-learning and Bayesian Optimisation. I am particularly interested in offline reinforcement learning with recent work in generalisation to unseen tasks, uncertainty quantification for pessimistic MDPs and offline learning from pixels. I obtained my undergraduate degree in Mathematics and Computer Science from the University of Oxford.
Interests
- Deep Reinforcement Learning
- Meta-Learning
- Bayesian Optimisation
Education
MMathCompSci in Mathematics and Computer Science, 2019
University of Oxford
Recent Publications
(2022).
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations.
Preprint.
(2021).
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From A Single Offline Environment.
In 38th International Conference on Machine Learning, 2021.
(2021).
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning.
In 38th International Conference on Machine Learning, 2021.
(2021).
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations.
In 34th Advances in Neural Information Processing Systems, 2021.
(2021).
Revisiting Design Choices in Offline Model Based Reinforcement Learning.
In ICML Reinforcement Learning for Real Life Workshop, 2021.
(2021).
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces.
In 38th International Conference on Machine Learning, 2021.
(2021).
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning.
In Journal of Machine Learning Research (to appear), 2021.