Description

Spatial navigation is the ability of organisms to extract and move towards a specific goal or location within their environment. This is a complex cognitive process that involves many different behaviors and strategies (Parra Barrero et al., 2023). We use reinforcement learning (RL) to model different aspects of navigation by training agents to learn to solve different navigation tasks through trial and error. Since RL operates in a closed loop, we can study the interaction of behavior and representations in artificial agents as they learn to solve tasks. To this end, we develop and maintain the software package CoBeL-RL (Diekmann et al., 2023), which provides tools to easily simulate a variety of scenarios and RL agents.

Within this framework, we offer bachelor's and master's theses. Depending on the level (bachelor or master) and preferences of the student, the project can be more technically oriented (developing new features for CoBeL-RL within the scope of answering a scientific question) or more an in-depth exploration of how artificial agents navigate and comparing them to navigation in animals.

Requirements

Python programming skills are required.

Literature

Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E.,  Wiskott, L., & Cheng, S. (2023). A map of spatial navigation for  neuroscience. In Neuroscience & Biobehavioral Reviews (Vol. 152,  p. 105200). Elsevier BV.  https://doi.org/10.1016/j.neubiorev.2023.105200

Diekmann, N., Vijayabaskaran, S., Zeng, X., Kappel, D., Menezes, M. C.,  & Cheng, S. (2023). CoBeL-RL: A neuroscience-oriented simulation  framework for complex behavior and learning. In Frontiers in  Neuroinformatics(Vol. 17). Frontiers Media SA.  https://doi.org/10.3389/fninf.2023.1134405

Contact

Prof. Dr. Sen Cheng and Sandhiya Vijayabaskaran (sandhiya.vijayabaskaran@rub.de)