Regularized Inverse Reinforcement LearningDownload PDF

Published: 12 Jan 2021, Last Modified: 05 May 2023ICLR 2021 SpotlightReaders: Everyone
Keywords: inverse reinforcement learning, reward learning, regularized markov decision processes, reinforcement learning
Abstract: 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 IRLapplies 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 solutions that are intractable in practice. We present theoretical backing for our proposed IRL method’s applicability to both discrete and continuous controls, empirically validating our performance on a variety of tasks.
One-sentence Summary: We propose tractable solutions of regularized IRL and algorithms to acquire those solutions.
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