In the reinforcement learning framework, the agent interacts with an environment and learns to select the best action sequence in order to achieve a predefined goal. The environment provides a performance evaluation by emitting a reward for optimization. The agents task is to maximise future reward by selecting the best available action. In concrete terms, this can for example be a computer program (agent) that plays an Atari game (environment) by trying to reach the highest possible score (emitted reward).
There are two approaches for reinforcement learning: (i) model-based reinforcement learning and (ii) model-free reinforcement learning.
In model-based reinforcement learning a (world) model is learned to accurately capture the environment mechanism. With this ability an agent is able to evaluate future outcomes by simulating them.
In model-free reinforcement learning the agent is learned directly on the experience gained by acting in the environment.
Our Publications on Reinforcement Learning:
- Wagner, Stefan, Michael Janschek, Tobias Uelwer, and Stefan Harmeling. "Learning to Plan via a Multi-step Policy Regression Method." In International Conference on Artificial Neural Networks, pp. 481-492. Springer, Cham, 2021.
- Robine, Jan, Tobias Uelwer, and Stefan Harmeling. "Smaller world models for reinforcement learning." arXiv preprint arXiv:2010.05767 (2020).
An inverse problem is the problem of finding an input to a system that produces a (given) set of observations. Depending on the definition of the system, inverse problems occure in different forms. Illustrative examples are (i) image denoising, where the task is to remove (additive) noise from an image, and (ii) image deblurring, which asks to remove image blur introduced, for example, by camera-shakes or poor focussing.
Many inverse problems are ill-posed, i.e., the solution is usually not unique. That means, finding a "realistic" solution is often very challenging and requires prior assumptions about the image (for example, that natural images often exhibit a reasonable amount of vertical and horizontal edges).
Other instance of inverse problems in image processing are: image dehazing, image deraining, image super-resolution, compressive sensing, and phase retrieval.
Some of our publications on inverse problems:
- Germer, Thomas, Tobias Uelwer, and Stefan Harmeling. "Deblurring Photographs of Characters Using Deep Neural Networks." Inverse Problems & Imaging (to appear). 2022.
- Uelwer, Tobias, Nick Rucks, and Stefan Harmeling. "A Closer Look at Reference Learning for Fourier Phase Retrieval." In NeurIPS 2021 Workshop on Deep Learning and Inverse Problems. 2021.
- Uelwer, Tobias, Alexander Oberstraß, and Stefan Harmeling. "Phase retrieval using conditional generative adversarial networks." In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 731-738. IEEE, 2021.
Search & People Search
Location & approach
The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A 45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B 1 / A 40. The best interstate exit to take from A 45 is “Dortmund-Eichlinghofen” (closer to South Campus), and from B 1 / A 40 “Dortmund-Dorstfeld” (closer to North Campus). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B 1-bridge leading into Dortmund.
To get from North Campus to South Campus by car, there is the connection via Vogelpothsweg/Baroper Straße. We recommend you leave your car on one of the parking lots at North Campus and use the H-Bahn (suspended monorail system), which conveniently connects the two campuses.
TU Dortmund University has its own train station (“Dortmund Universität”). From there, suburban trains (S-Bahn) leave for Dortmund main station (“Dortmund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.
You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop “Dortmund Kampstraße”. From there, take U43 or U44 to the stop “Dortmund Wittener Straße”. Switch to bus line 447 and get off at “Dortmund Universität S”.
The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.
The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on North Campus. One (“Dortmund Universität S”) is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Campus) Eichlinghofen. The other station is located at the dining hall at North Campus and offers a direct connection to South Campus every five minutes.
The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent “Technologiepark”.
Site Map of TU Dortmund University (Second Page in English).