  Inverse Reinforcement Learning (IRL) is the problem of finding a reward
function which describes observed/known expert behavior. IRL is useful for
automated control in situations where the reward function is difficult to
specify manually, which impedes reinforcement learning. We provide a new IRL
algorithm for the continuous state space setting with unknown transition
dynamics by modeling the system using a basis of orthonormal functions. We
provide a proof of correctness and formal guarantees on the sample and time
complexity of our algorithm.
