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.
My current research is focused on developing principled but scalable Bayesian reinforcement learning methods that address the most significant of these challenges: exploration, planning, and generalization.
More information about my work is available here.
- R. Sasso, M. Conserva, P. Rauber. “Posterior Sampling for Deep Reinforcement Learning”, International Conference on Machine Learning (ICML), 2023.
- M. Conserva, P. Rauber. “Hardness in Markov Decision Processes: Theory and Practice”, Conference on Neural Information Processing Systems (NeurIPS), 2022.
- P. Rauber*, A. Ramesh*, M. Conserva, J. Schmidhuber. “Recurrent Neural-Linear Posterior Sampling for Non-Stationary Contextual Bandits”, Neural Computation, 2022.
- P. Rauber, A. Ummadisingu, F. Mutz, J. Schmidhuber. “Hindsight Policy Gradients”, International Conference on Learning Representations (ICLR), 2019.
If you would like to pursue a PhD under my supervision, please send me a message with your curriculum vitae and a brief description of your research goals after reading this.