Search Results for author: Matthew Fellows

Found 3 papers, 1 papers with code

Bayesian Bellman Operators

no code implementations • NeurIPS 2021 • 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 characterise the uncertainty in the Bellman operator.

Continuous Control Reinforcement Learning (RL)

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VIREL: A Variational Inference Framework for Reinforcement Learning

1 code implementation • NeurIPS 2019 • Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson

This gives VIREL a mode-seeking form of KL divergence, the ability to learn deterministic optimal polices naturally from inference and the ability to optimise value functions and policies in separate, iterative steps.

reinforcement-learning Reinforcement Learning (RL) +1

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Fourier Policy Gradients

no code implementations • ICML 2018 • Matthew Fellows, Kamil Ciosek, Shimon Whiteson

We propose a new way of deriving policy gradient updates for reinforcement learning.

Reinforcement Learning (RL)

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