39 citations · 140 across the 12 of their papers we have counts for
14 papers
Variational Inference for Model-Free and Model-Based Reinforcement Learning
Felix Leibfried
Variational inference (VI) is a specific type of approximate Bayesian inference that approximates an intractable posterior distribution with a tractable one. VI casts the inference…
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
John McLeod, Hrvoje Stojic, Vincent Adam +4
In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-ef…
GPflux: A Library for Deep Gaussian Processes
Vincent Dutordoir, Hugh Salimbeni, Eric Hambro +7
We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the v…
A Tutorial on Sparse Gaussian Processes and Variational Inference
Felix Leibfried, Vincent Dutordoir, ST John +1
Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems. For example, if we consider regre…
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning
Felix Leibfried, Jordi Grau-Moya
Cumulative entropy regularization introduces a regulatory signal to the reinforcement learning (RL) problem that encourages policies with high-entropy actions, which is equivalent…
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya
Empowerment is an information-theoretic method that can be used to intrinsically motivate learning agents. It attempts to maximize an agent's control over the environment by encour…