OfflineReinforcementLearningwithDifferential Privacy

Since offline RL does not require access to the environment, it can be applied to problems where interaction with environment is infeasible,e.g., when collecting new data is costly (trade or finance [Zhang et al., 2020]), risky (autonomous driving [Sallab et al., 2017]) or illegal / unethical (healthcare [Raghu etal.,2017]).