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Continual Few-shot reinforcement learning

Primary supervisor

Levin Kuhlmann


  • Gideon Kowadlo

This project takes a different approach to RL, inspired by evidence that Hippocampus replays to the frontal cortex directly. It is likely used for model building, as opposed to the mainstream view in cognitive science and ML - where 'experience replay' ultimately improves policy. The predicted benefits are sample efficiency, better ability to generalize to new tasks and an ability to learn new tasks without forgetting old ones. The project objective is to improve biological models and advance state-of-the-art in continual reinforcement learning.


Student cohort

Double Semester

Required knowledge

Machine Learning, Deep Learning or some knowledge and willingness to learn. Must have Python and some experience with PyTorch or Tensorflow.