  Inverse reinforcement learning (IRL) is the problem of inferring the reward
function of an agent, given its policy or observed behavior. Analogous to RL,
IRL is perceived both as a problem and as a class of methods. By categorically
surveying the current literature in IRL, this article serves as a reference for
researchers and practitioners of machine learning and beyond to understand the
challenges of IRL and select the approaches best suited for the problem on
hand. The survey formally introduces the IRL problem along with its central
challenges such as the difficulty in performing accurate inference and its
generalizability, its sensitivity to prior knowledge, and the disproportionate
growth in solution complexity with problem size. The article elaborates how the
current methods mitigate these challenges. We further discuss the extensions to
traditional IRL methods for handling: inaccurate and incomplete perception, an
incomplete model, multiple reward functions, and nonlinear reward functions.
This survey concludes the discussion with some broad advances in the research
area and currently open research questions.
