Inverse Reinforcement Learning Under Noisy Observations

We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, noisy observations of the trajectory are available. We generalize the previous method of expectation-maximization for inverse reinforcement learning, which allows the trajectory of the expert to be partially hidden from the learner, to incorporate an observation model.