Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimality Principle. - Tim Genewein; Felix Leibfried; Jordi Grau-Moya; Daniel Alexander Braun (Frontiers Robotics AI, 2015)

A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker. - Felix Leibfried; Daniel A. Braun 001 (Neural Comput., 2015)

Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes. - Jordi Grau-Moya; Felix Leibfried; Tim Genewein; Daniel A. Braun 001 (ECML/PKDD (2), 2016)

Bounded Rational Decision-Making in Feedforward Neural Networks. - Felix Leibfried; Daniel A. Braun 001 (UAI, 2016)

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games. - Felix Leibfried; Nate Kushman; Katja Hofmann (CoRR, 2016)

An information-theoretic on-line update principle for perception-action coupling. - Zhen Peng 004; Tim Genewein; Felix Leibfried; Daniel A. Braun 001 (IROS, 2017)

An Information-Theoretic Optimality Principle for Deep Reinforcement Learning. - Felix Leibfried; Jordi Grau-Moya; Haitham Bou-Ammar (CoRR, 2017)

Decision-making with limited information-processing resources in single-agent and multi-agent systems. - Felix Leibfried (2017)

Balancing Two-Player Stochastic Games with Soft Q-Learning. - Jordi Grau-Moya; Felix Leibfried; Haitham Bou-Ammar (IJCAI, 2018)

Model-Based Stabilisation of Deep Reinforcement Learning. - Felix Leibfried; Rasul Tutunov; Peter Vrancx; Haitham Bou-Ammar (CoRR, 2018)

Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning. - Felix Leibfried; Jordi Grau-Moya (CoRL, 2019)

Soft Q-Learning with Mutual-Information Regularization. - Jordi Grau-Moya; Felix Leibfried; Peter Vrancx (ICLR (Poster), 2019)

A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment. - Felix Leibfried; Sergio Pascual-Diaz; Jordi Grau-Moya (NeurIPS, 2019)

Uncertainty in Neural Networks: Approximately Bayesian Ensembling. - Tim Pearce; Felix Leibfried; Alexandra Brintrup (AISTATS, 2020)

A Tutorial on Sparse Gaussian Processes and Variational Inference. - Felix Leibfried; Vincent Dutordoir; S. T. John; Nicolas Durrande (CoRR, 2020)

Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow. - John McLeod; Hrvoje Stojic; Vincent Adam; Dongho Kim; Jordi Grau-Moya; Peter Vrancx; Felix Leibfried (CoRR, 2021)

GPflux: A Library for Deep Gaussian Processes. - Vincent Dutordoir; Hugh Salimbeni; Eric Hambro; John McLeod; Felix Leibfried; Artem Artemev; Mark van der Wilk; James Hensman; Marc Peter Deisenroth; S. T. John (CoRR, 2021)

Variational Inference for Model-Free and Model-Based Reinforcement Learning. - Felix Leibfried (CoRR, 2022)