Pascal Klink

Quick Info

Research Interests

Reinforcement Learning (under complete- and partial observability), Transfer- and Curriculum Learning, Bayesian Inference, Robotics, Optimal Control.

Contact Information

Mail. TU Darmstadt, FB-Informatik, FG-IAS, Hochschulstr. 10, 64289 Darmstadt
Office. Room E327, Building S2|02
work+49-6151-16-25371


Pascal joined the Intelligent Autonomous Systems Group in May 2019 as a PhD student and is working on the ROBOLEAP project, developing methods for reinforcement learning in unstructured, partially observable real world environments. In general, his research interests revolve around various aspects of reinforcement learning that, in his opinion, limit its applicability to real world settings - two of them being partial observability and knowledge transfer. He is convinced that truly intelligent systems need to be able to learn in environments with incomplete observations of the environment and reuse previously acquired knowledge to speed up and bootstrap learning in new situations.

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. 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).   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Klink, P.; D`Eramo, C.; Peters, J.; Pajarinen, J. (2022). Boosted Curriculum Reinforcement Learning, International Conference on Learning Representations (ICLR).   Download Article [PDF]   BibTeX Reference [BibTex]
  3. 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).   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Klink, P.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2020). Self-Paced Deep Reinforcement Learning, Advances in Neural Information Processing Systems (NIPS / NeurIPS).   Download Article [PDF]   BibTeX Reference [BibTex]
  5. Klink, P.; Abdulsamad, H.; Belousov, B.; Peters, J. (2019). Self-Paced Contextual Reinforcement Learning, Proceedings of the 3rd Conference on Robot Learning (CoRL).   Download Article [PDF]   BibTeX Reference [BibTex]
  6. Klink, P.; Peters, J. (2019). Measuring Similarities between Markov Decision Processes, 4th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM).   BibTeX Reference [BibTex]

Approximate Bayesian Inference for Machine Learning

  1. 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).   Download Article [PDF]   BibTeX Reference [BibTex]
  2. 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).   Download Article [PDF]   BibTeX Reference [BibTex]
  3. 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).   Download Article [PDF]   BibTeX Reference [BibTex]

Tree Search

  1. 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).   Download Article [PDF]   BibTeX Reference [BibTex]

Books

  1. Belousov, B.; Abdulsamad H.; Klink, P.; Parisi, S.; Peters, J. (2021). Reinforcement Learning Algorithms: Analysis and Applications, Studies in Computational Intelligence, Springer International Publishing.   Download Article [PDF]   BibTeX Reference [BibTex]


  

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