Roll-Drop: accounting for observation noise with a single parameter

Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala +2

This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noi…