Average Reward Optimization with Multiple Discounting Reinforcement Learners. - Chris Reinke; Eiji Uchibe; Kenji Doya (ICONIP (1), 2017)

Intrinsically Motivated Exploration for Automated Discovery of Patterns in Morphogenetic Systems. - Chris Reinke; Mayalen Etcheverry; Pierre-Yves Oudeyer (CoRR, 2019)

Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems. - Chris Reinke; Mayalen Etcheverry; Pierre-Yves Oudeyer (ICLR, 2020)

Time Adaptive Reinforcement Learning. - Chris Reinke (CoRR, 2020)

Progressive growing of self-organized hierarchical representations for exploration. - Mayalen Etcheverry; Pierre-Yves Oudeyer; Chris Reinke (CoRR, 2020)

Xi-Learning: Successor Feature Transfer Learning for General Reward Functions. - Chris Reinke; Xavier Alameda-Pineda (CoRR, 2021)

Successor Feature Neural Episodic Control. - David Emukpere; Xavier Alameda-Pineda; Chris Reinke (CoRR, 2021)

Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputs. - Basavasagar Patil; Xavier Alameda-Pineda; Chris Reinke (CoRR, 2023)

Successor Feature Representations. - Chris Reinke; Xavier Alameda-Pineda (Trans. Mach. Learn. Res., 2023)

Variational meta reinforcement learning for social robotics. - Anand Ballou; Xavier Alameda-Pineda; Chris Reinke (Appl. Intell., 2023)

Socially Pertinent Robots in Gerontological Healthcare. - Xavier Alameda-Pineda; Angus Addlesee; Daniel Hernández García; Chris Reinke; Soraya Arias; Federica Arrigoni; Alex Auternaud; Lauriane Blavette; Cigdem Beyan; Luis G. Camara; Ohad Cohen; Alessandro Conti; et al. (CoRR, 2024)