  Inverse optimal control, also known as inverse reinforcement learning, is the
problem of recovering an unknown reward function in a Markov decision process
from expert demonstrations of the optimal policy. We introduce a probabilistic
inverse optimal control algorithm that scales gracefully with task
dimensionality, and is suitable for large, continuous domains where even
computing a full policy is impractical. By using a local approximation of the
reward function, our method can also drop the assumption that the
demonstrations are globally optimal, requiring only local optimality. This
allows it to learn from examples that are unsuitable for prior methods.
