Search Results for author: Felix Leibfried

Found 15 papers, 3 papers with code

Variational Inference for Model-Free and Model-Based Reinforcement Learning

no code implementations4 Sep 2022 Felix Leibfried

And second, in model-based RL where agents aim to learn about the environment they are operating in, the model-learning part can be naturally phrased as an inference problem over the process that governs environment dynamics.

Bayesian Inference Model-based Reinforcement Learning +3

Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow

2 code implementations26 Mar 2021 John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried

This paves the way for new research directions, e. g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.

Model-based Reinforcement Learning reinforcement-learning

A Tutorial on Sparse Gaussian Processes and Variational Inference

no code implementations27 Dec 2020 Felix Leibfried, Vincent Dutordoir, ST John, Nicolas Durrande

In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood.

Bayesian Inference Gaussian Processes +1

Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning

no code implementations11 Sep 2019 Felix Leibfried, Jordi Grau-Moya

While this has been initially proposed for Markov Decision Processes (MDPs) in tabular settings, it was recently shown that a similar principle leads to significant improvements over vanilla SQL in RL for high-dimensional domains with discrete actions and function approximators.


Uncertainty in Neural Networks: Approximately Bayesian Ensembling

2 code implementations12 Oct 2018 Tim Pearce, Felix Leibfried, Alexandra Brintrup, Mohamed Zaki, Andy Neely

Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian.

Bayesian Inference General Classification +1

Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks

no code implementations6 Sep 2018 Felix Leibfried, Peter Vrancx

This paper proposes a new optimization objective for value-based deep reinforcement learning.


An information-theoretic on-line update principle for perception-action coupling

no code implementations16 Apr 2018 Zhen Peng, Tim Genewein, Felix Leibfried, Daniel A. Braun

Here we consider perception and action as two serial information channels with limited information-processing capacity.

Balancing Two-Player Stochastic Games with Soft Q-Learning

no code implementations9 Feb 2018 Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar

Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers.

Q-Learning reinforcement-learning

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

no code implementations21 Nov 2016 Felix Leibfried, Nate Kushman, Katja Hofmann

Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown.

Atari Games Model-based Reinforcement Learning +1

Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes

no code implementations7 Apr 2016 Jordi Grau-Moya, Felix Leibfried, Tim Genewein, Daniel A. Braun

As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning.

Bounded Rational Decision-Making in Feedforward Neural Networks

no code implementations26 Feb 2016 Felix Leibfried, Daniel Alexander Braun

Bounded rational decision-makers transform sensory input into motor output under limited computational resources.

Decision Making General Classification

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