Pascal Klink

Pascal joined the Intelligent Autonomous Systems Group in May 2019 as a PhD student working on the ROBOLEAP project. He defended his PhD thesis in November 2023. In general, his research interests revolve around various aspects of reinforcement learning that, in his opinion, will be crucial to tackling challenging reinforcement learning problems - two of them being knowledge transfer and learning via curricula. He is convinced that learning systems need to be able to reuse previously acquired knowledge to speed up and bootstrap learning in new situations in order to tackle the most challenging tasks.

Before starting his PhD, Pascal completed his Bachelor's degree in Computer Science and Master's degree in Autonomous Systems at the Technische Universitaet Darmstadt. He wrote his Master's thesis on "Generalization and Transferability in Reinforcement Learning" supervised by Hany Abdulsamad, Boris Belousov and Jan Peters, where he investigated concepts from the domain of numerical continuation, parameteric programming and concurrent systems theory for the task of knowledge transfer and finally developed a method for autonomous curriculum generation for reinforcement learning problems.

Key References

Curriculum- and Transfer Learning

1. •       Bib
     Klink, P.; Wolf, F.; Ploeger, K.; Peter, J.; Pajarinen, J. (submitted). Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning, Submitted to the IEEE Transactions on Robotics (T-Ro).
2. •       Bib
     Klink, P.; D'Eramo, C.; Peters, J.; Pajarinen, J. (submitted). On the Benefit of Optimal Transport for Curriculum Reinforcement Learning, Submitted to the IEEE Transaction on Pattern Analysis and Machine Intelligence (T-PAMI).
3. •     Bib
     Klink, P.; Yang, H.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2022). Curriculum Reinforcement Learning via Constrained Optimal Transport, International Conference on Machine Learning (ICML).
4. •     Bib
     Klink, P.; D`Eramo, C.; Peters, J.; Pajarinen, J. (2022). Boosted Curriculum Reinforcement Learning, International Conference on Learning Representations (ICLR).
5. •     Bib
     Klink, P.; Abdulsamad, H.; Belousov, B.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2021). A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning, Journal of Machine Learning Research (JMLR).
6. •     Bib
     Klink, P.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2020). Self-Paced Deep Reinforcement Learning, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
7. •     Bib
     Klink, P.; Abdulsamad, H.; Belousov, B.; Peters, J. (2019). Self-Paced Contextual Reinforcement Learning, Proceedings of the 3rd Conference on Robot Learning (CoRL).
8. •   Bib
     Klink, P.; Peters, J. (2019). Measuring Similarities between Markov Decision Processes, 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM).

Approximate Bayesian Inference for Machine Learning

1. •     Bib
     Abdulsamad, H.; Nickl, P.; Klink, P.; Peters, J. (in press). Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).
2. •     Bib
     Abdulsamad, H.; Nickl, P.; Klink, P.; Peters, J. (2021). A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
3. •     Bib
     Watson, J.; Lin, J. A.; Klink, P.; Peters, J. (2021). Neural Linear Models with Functional Gaussian Process Priors, 3rd Symposium on Advances in Approximate Bayesian Inference (AABI).
4. •     Bib
     Watson, J.; Lin J. A.; Klink, P.; Pajarinen, J.; Peters, J. (2021). Latent Derivative Bayesian Last Layer Networks, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

Tree Search

1. •       Bib
     Dam, T.; Klink, P.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2020). Generalized Mean Estimation in Monte-Carlo Tree Search, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).

Books

1. •     Bib
     Belousov, B.; Abdulsamad H.; Klink, P.; Parisi, S.; Peters, J. (2021). Reinforcement Learning Algorithms: Analysis and Applications, Studies in Computational Intelligence, Springer International Publishing.