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.


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