ID 2365: Deep Reinforcement Learning for Musculoskeletal Models

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The goal of this thesis is to find a controller that can reproduce realistic and robust standing / walking behaviour of a neuromusculoskeletal model based on physiological feedback. In a first step, this should be done using imitation learning from data that we recorded in a previous study. If that works, the next idea would be to incorporate a higher level controller that computes the best action from multiple sub-policies which capture different movement strategies of humans.


Implementation of an imitation-based reward function for standing and walking for deep reinforcement learning.

Implementation of a multi-level control structure for human movements using deep reinforcement learning

Clear and structured code documentation



Experience in Reinforcement Learning / Deep Learning

Independent working style



Sophie Fleischmann, M. Sc.

Researcher & PhD Candidate