title: Learning Robust Rewards with Adverserial Inverse Reinforcement Learning
abstract: Reinforcement learning provides a powerful and general framework for decision
  making and control, but its application in practice is often hindered by the need
  for extensive feature and reward engineering. Deep reinforcement learning methods
  can remove the need for explicit engineering of policy or value features, but
  still require a manually specified reward function. Inverse reinforcement learning
  holds the promise of automatic reward acquisition, but has proven exceptionally
  difficult to apply to large, high-dimensional problems with unknown dynamics. In
  this work, we propose AIRL, a practical and scalable inverse reinforcement learning
  algorithm based on an adversarial reward learning formulation that is competitive
  with direct imitation learning algorithms. Additionally, we show that AIRL is
  able to recover portable reward functions that are robust to changes in dynamics,
  enabling us to learn policies even under significant variation in the environment
  seen during training.
authors: Justin Fu Katie Luo Sergey Levine
hIndex: Sergey Levine 119