Regularized Inverse Reinforcement Learning
This work addresses a specific limitation in IRL for imitation learning, offering a more general and tractable approach compared to existing methods.
The paper tackles the problem of degenerate solutions in Inverse Reinforcement Learning (IRL) by proposing a regularized IRL method that applies strongly convex regularizers to avoid arbitrary constant rewards, with empirical validation on various tasks.
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.