I'm a postdoc at the Helmut-Schmidt-University. My current research addresses the utilization of reinforcement learning for complex task architectures. I'm also investigating quantitative methods to enhance large behavioral data sets for personality psychology research.

I was co-author of the TensorForce reinforcement learning library. Currently, I'm working with two friends on RLgraph, a new, modular, backend-independent enterprise-ready reinforcement learning library.

Publications in peer-reviewed journals

  • Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2019). Measuring musical preferences from listening behavior: Data from 1 million people and 200,000 songs. Psychology of Music. Manuscript accepted.
  • Schaarschmidt, M., Mika, S., Fricke, K., & Yoneki, E. (2019). RLgraph: Modular Computation Graphs for Deep Reinforcement Learning. Proceedings of the 2nd Conference on Systems and Machine Learning (SysML). [bib] [link] [pre-print]
  • Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2018). Computer-based music feature analysis mirrors human perception and can be used to measure individual music preference. Journal of Research in Personality, 75, 94-102. [bib] [link]
  • Fricke, K. R., & Herzberg, P. Y. (2017). Personality and self-reported preference for music genres and attributes in a German-speaking sample. Journal of Research in Personality, 68, 114-123. [bib] [link]
  • Rohenkohl, A. C., De Schepper, J., Vanderfaeillie, J., Fricke, K., Hendrickx, S., Lagrou, K., ... & Quitmann, J. (2014). Validation of the Flemish version of the Quality of Life in Short Stature Youth (QoLISSY) questionnaire. Acta Clinica Belgica, 69, 177-182. [bib] [link]

Upcoming publications

  • Schaarschmidt, M., Fricke, K. R., & Yoneki, E. (2019). Wield: Systematic Reinforcement Learning With Progressive Randomization. Manuscript submitted for publication. [pre-print]
  • Fricke, K. R., & Herzberg, P. Y. (2019). Know your big data: De-biasing subsamples of large datasets for personality research using importance sampling and kNN matching. Manuscript submitted for publication. [pre-print]
  • Greenberg, D. M., & Fricke, K. R. (2019). Decreasing stress through a spatial audio and immersive 3D environment: Implications for clinical and medical settings. Manuscript submitted for publication.
  • Greenberg, D. M., Matz, S. C., Schwartz, H. A., & Fricke, K. R. (2019). The self-congruity effect of music. Manuscript submitted for publication.
  • Schaarschmidt, M., Kuhnle, A., Ellis, B., Fricke, K., Gessert, F., & Yoneki, E. (2018). LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations. arXiv preprint arXiv:1808.07903. [bib] [pre-print]

Conference talks

  • Fricke, K. R., Wildfang, S., & Herzberg, P. Y. (2019). Enhancing big data for the exploration of user personality characteristics of online music streaming services users. In Melanie Viola Partsch (Chair), Advanced Modeling. Paper session conducted at the DPPD conference, Dresden.
  • Fricke, K. R., & Herzberg, P. Y. (2018). Messen von Musikpr√§ferenz aus Playback-Statistiken von Musikstreamingdiensten. In S. J. Mayer (Chair), Pers√∂nlichkeitsdimensionen. Symposium conducted at the DGPS conference, Frankfurt (Main).