A Practitioner's Guide to Maximum Causal Entropy Inverse Reinforcement Learning, Starting from Markov Decision Processes
CARNEGIE-MELLON UNIV PITTSBURGH PA PITTSBURGH United States
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This guide is meant to describe both the semantics and mechanics of the Maximum Causal Entropy MaxCausalEnt Inverse Reinforcement Learning IRL algorithm 4. Throughout the remainder of this document, we provide a measure of formal definition of the algorithm, starting from the basics, adding some intuition as we go. We intentionally skip a large amount of prior, related, and theoretic work that motivates and contextualizes the algorithm. For further reading on these subjects, see the works referenced throughout. Finally, the gray break out boxes are notes meant to provide broader context, and can be skipped without breaking the ow of the guide.
- Statistics and Probability