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A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
Inverse reinforcement learning is the problem of inferring the reward
function of an observed agent, given its policy or behavior. Researchers
perceive IRL 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 in machine learning 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
which include accurate inference, generalizability, correctness of prior
knowledge, and growth in solution complexity with problem size. The article
elaborates how the current methods mitigate these challenges. We further
discuss the extensions of traditional IRL methods: (i) inaccurate and
incomplete perception, (ii) incomplete model, (iii) multiple rewards, and (iv)
non-linear reward functions. This discussion concludes with some broad advances
in the research area and currently open research questions