Curriculum/Vitae

 

Since 2017   Research associate at the Chair for Organic Computing

 

2014-2017    Master’s degree in Computer Science at the University of Augsburg


2010-2014    Bachelor’s degree in Business Information Systems at the University of Ausgurg

Research foci

My research is mainly concerned with Reinforcement Learning (RL). The fokus thereby lies on fundamental research in deep RL.

 

The Deep Q-Network Algorithm (DQN) as a prominent representative of deep RL algorithms uses (not as the only one) a so called Experience Replay (ER). This can be imagined as a capped List (FiFo buffer) of experienced transistions (state, action, reward, next state). My research is fokused on the ER and possible combinations of it with interpolation techniques. The default ER is filled slowly with real experiences, and I look for possibilities of creating synthetic transitions using interpolation to assist the training.

Courses / teaching

(applied filters: | Semester: current | Institution: Organic Computing | Lehrende: Wenzel Pilar von Pilchau | Typen: Seminar, Praktikum)
name semester type
Praktikum zu Selbstlernende Systeme WS 2019/20 Praktikum
Seminar über Ad-hoc- und Sensornetze WS 2019/20 Seminar
Seminar über Organic Computing WS 2019/20 Seminar

Publications

  • Averaging rewards as a first approach towards Interpolated Experience Replay. Wenzel Pilar von Pilchau. In: Draude, C., Lange, M. & Sick, B. (Hrsg.), INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge). Bonn: Gesellschaft für Informatik e.V.. (S. 493-506). DOI:
  • Combining Machine Learning with Blockchain: Benefits, Approaches and Challenges. Wenzel Pilar von Pilchau and Jörg Hähner. In Organic Computing - Doctoral Dissertation Colloquium (OC-DDC), 21. - 22.06.2018, Würzburg, Germany

Search