Research

The research conducted by the Chair of Reinforcement Learning and Computational Decision-Making revolves around the problem of how agents can efficiently acquire expert skills that account for the complexity of the real world. To answer this question, we investigate lightweight methods to obtain adaptive autonomous agents, focusing on several RL topics including multi-task, curriculum, adversarial, options, and multi-agent RL.

If you are interested to collaborate with us or if you are interested in a visit please have a look at current opportunities.