  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 adverserial inverse
reinforcement learning (AIRL), a practical and scalable inverse reinforcement
learning algorithm based on an adversarial reward learning formulation. We
demonstrate that AIRL is able to recover reward functions that are robust to
changes in dynamics, enabling us to learn policies even under significant
variation in the environment seen during training. Our experiments show that
AIRL greatly outperforms prior methods in these transfer settings.

Machine Learning