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09/04/2022
Variational Inference for Model-Free and Model-Based Reinforcement Learning
Variational inference (VI) is a specific type of approximate Bayesian in...
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04/12/2021
GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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03/26/2021
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
In the past decade, model-free reinforcement learning (RL) has provided ...
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12/27/2020
A Tutorial on Sparse Gaussian Processes and Variational Inference
Gaussian processes (GPs) provide a framework for Bayesian inference that...
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09/11/2019
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning
Cumulative entropy regularization introduces a regulatory signal to the ...
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07/26/2019
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
Empowerment is an information-theoretic method that can be used to intri...
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09/06/2018
Model-Based Stabilisation of Deep Reinforcement Learning
Though successful in high-dimensional domains, deep reinforcement learni...
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04/16/2018
An information-theoretic on-line update principle for perception-action coupling
Inspired by findings of sensorimotor coupling in humans and animals, the...
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02/09/2018
Balancing Two-Player Stochastic Games with Soft Q-Learning
Within the context of video games the notion of perfectly rational agent...
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08/06/2017
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
In this paper, we methodologically address the problem of cumulative rew...
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11/21/2016
A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games
Reinforcement learning is concerned with identifying reward-maximizing b...
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04/07/2016
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
Information-theoretic principles for learning and acting have been propo...
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02/26/2016
