ActiveExplorationfor InverseReinforcementLearning

Instead of using an explicit reward function, Inverse Reinforcement Learning (IRL; Ng et al., 2000) seeks to recover the reward by observing anexpert,e.g.,anhuman whoalready knowshowtoperform atask. However,most existing IRL algorithms assume that the transition model, and in some cases, the expert's policy, areknown.