Marginalized Importance Sampling for Off-Environment Policy Evaluation

Pulkit Katdare, Nan Jiang, Katherine Driggs-Campbell

Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in si…