Dr Paulo Rauber
Lecturer in Artificial Intelligence
School of Electronic Engineering and Computer Science
Queen Mary University of London
Research
Artificial Intelligence, Machine Learning, Reinforcement Learning, Formalized Mathematics
Interests
I am a lecturer in Artificial Intelligence at Queen Mary University of London. Before becoming a lecturer, I was a postdoctoral researcher in the Swiss AI lab working on reinforcement learning under the supervision of Jürgen Schmidhuber.
I believe that intelligence should be defined as a measure of the ability of an agent to achieve goals in a wide range of environments, which makes reinforcement learning an excellent framework to study many challenges that intelligent agents are bound to face.
Publications of specific relevance to the Centre for Multimodal AI
Publications of specific relevance to the Centre for Multimodal AI
2023
Posterior Sampling for Deep Reinforcement LearningSasso R Conserva M Rauber P
International Conference on Machine Learning. vol. 202, 30042-30061.
01-01-20232022
Hardness in Markov Decision Processes: Theory and PracticeConserva M
Advances in Neural Information Processing Systems.
24-10-2022 Recurrent Neural-Linear Posterior Sampling
for Nonstationary Contextual BanditsRamesh A Rauber P Conserva M
Neural Computation,
Massachusetts Institute of Technology Press vol. 34, 1-41.
09-09-2022 Hardness in Markov Decision Processes: Theory and PracticeConserva M
Neural Information Processing Systems. vol. 35
01-01-2022 2021
Reinforcement Learning in Sparse-Reward Environments with Hindsight Policy GradientsRauber P Ummadisingu A Mutz F
Neural Computation,
Massachusetts Institute of Technology Press (MIT Press) 13-05-2021 2019
Hindsight policy gradientsRauber P Ummadisingu A Mutz F
ICLR 2019., 1-1.
01-01-2019
Research Group
PhD Students
- Michelangelo Conserva
Towards a Systematic Understanding of Environments Hardness in Reinforcement Learning - Remo Sasso
Scalable and Efficient Exploration For Model-Based Reinforcement Learning - Connor Watts
Towards Interpreting in-Context Reinforcement Learning - Linjie Xu
Efficient Data Usage in Planning and Reinforcement Learning
