  Reinforcement learning can acquire complex behaviors from high-level
specifications. However, defining a cost function that can be optimized
effectively and encodes the correct task is challenging in practice. We explore
how inverse optimal control (IOC) can be used to learn behaviors from
demonstrations, with applications to torque control of high-dimensional robotic
systems. Our method addresses two key challenges in inverse optimal control:
first, the need for informative features and effective regularization to impose
structure on the cost, and second, the difficulty of learning the cost function
under unknown dynamics for high-dimensional continuous systems. To address the
former challenge, we present an algorithm capable of learning arbitrary
nonlinear cost functions, such as neural networks, without meticulous feature
engineering. To address the latter challenge, we formulate an efficient
sample-based approximation for MaxEnt IOC. We evaluate our method on a series
of simulated tasks and real-world robotic manipulation problems, demonstrating
substantial improvement over prior methods both in terms of task complexity and
sample efficiency.
