Students develop an algorithmic understanding of modern reinforcement learning methods (RL) beyond classical tabular towards deep RL approaches. They are able to analyze, derive, and extend advanced RL algorithms, understand their theoretical guarantees and limitations, and apply them to high-dimensional, partially observable, and real-world robotic systems. Students can critically assess state-of-the-art research literature and independently design advanced RL solutions for complex sequential decision-making problems.
