Second-Order Methods in Deep RL

The project seeks to apply second-order methods in complex environments (such as Atari games) and compare their performances with first-order methods empirically in terms of sample complexity and robustness to changes in initializations.

Requirements:

Good knowledge of reinforcement learning

Strong Python programming skills

Experience with training deep RL agents with ML libraries such as Pytorch

If interested, please send your CV and a transcript of your grades to saber.salehkaleybar@epfl.ch