Reinforcement learning (RL) has been successful in applications, but theory has not been able to guarantee reliability and robustness of the used algorithms. One reason is that RL theory focuses on optimization, while practical RL problems are task-oriented so that optimality doesn't play any role. We aim at a restart of RL theory by replacing the optimality paradigm by a criterion based on satisficing, which will alleviate the development and analysis of algorithms. If successful our research project will finally develop RL theory that is useful and applicable to practical RL problems.
We are looking for a PostDoc with strong background in mathematics (such as dynamic programming, probability theory, statistics, optimization, OR) and interest in reinforcement learning. Highly qualified PhD candidates might be considered as well.