title: Learning Invariant Reward Functions through Trajectory Interventions
abstract: Inverse reinforcement learning methods aim to retrieve the reward function of
  a Markov decision process based on a dataset of expert demonstrations. The
  commonplace scarcity of such demonstrations potentially leads to the absorption of
  spurious correlations in the data by the learning model, which as a result, exhibits
  behavioural overfitting to the expert dataset when trained on the obtained reward
  function. We study the generalization properties of the maximum entropy method
  for solving the inverse reinforcement learning problem for both exact and approximate
  formulations and demonstrate that by applying an instantiation of the invariant
  risk minimization principle, we can recover reward functions which induce better
  performing policies across domains in the transfer setting.
authors: Ivan Ovinnikov Eugene Bykovets Joachim M. Buhmann
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