However, such methods require expensive resources such as domain experts to hand-code the rules, or a corpus of expertlayperson interactions to train on.
we present a corpus-driven framework using which a user-adaptive REG policy can be learned using RL from a small corpus of non-adaptive humanmachine interaction.