  Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability
to imitate expert behavior by acquiring reward functions that explain the
expert's decisions. Regularized IRL applies strongly convex regularizers to the
learner's policy in order to avoid the expert's behavior being rationalized by
arbitrary constant rewards, also known as degenerate solutions. We propose
tractable solutions, and practical methods to obtain them, for regularized IRL.
Current methods are restricted to the maximum-entropy IRL framework, limiting
them to Shannon-entropy regularizers, as well as proposing the solutions that
are intractable in practice. We present theoretical backing for our proposed
IRL method's applicability for both discrete and continuous controls,
empirically validating our performance on a variety of tasks.
