This internship studies the applications of Hierarchical Reinforcement Learning methods in robotics: Deploying autonomous robots in real world environments typically introduces multiple difficulties among which is the size of the observable space and the length of the required tasks. Reinforcement Learning typically helps agents solve decision making problems by autonomously discovering successful behaviours and learning them. But these methods are known to struggle with long and complex tasks. Hierarchical Reinforcement Learning extend this paradigm to decompose these problems into easier subproblems with High-level agents determining which subtasks need to be accomplished, and Low-level agent learning to achieve them. During this internship, the intern will : Get acquainted with the state of art in Hierarchical Reinforcement Learning including the most notable algorithms [1, 2, 3], the challenges they solve and their limitations. Reimplement some of these approaches and validate their results in robotics simulated environments such as iGibson [4]. Establish an experimental comparison of these methods with respect to some research hypothesis. The intern is expected to also collaborate with a PhD student whose work is closely related to this topic.