29 citations · 36 across the 3 of their papers we have counts for
3 papers
Bayesian Bellman Operators
Matthew Fellows, Kristian Hartikainen, Shimon Whiteson
We introduce a novel perspective on Bayesian reinforcement learning (RL); whereas existing approaches infer a posterior over the transition distribution or Q-function, we character…
VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner +1
Applying probabilistic models to reinforcement learning (RL) enables the application of powerful optimisation tools such as variational inference to RL. However, existing inference…
Fourier Policy Gradients
Matthew Fellows, Kamil Ciosek, Shimon Whiteson
We propose a new way of deriving policy gradient updates for reinforcement learning. Our technique, based on Fourier analysis, recasts integrals that arise with expected policy gra…