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Inverse Reinforcement Learning in Tracking Control Based on Inverse Optimal Control by Wenqian Xue; Patrik Kolaric; Jialu Fan; Bosen Lian; Tianyou Chai; Frank L. Lewis is a Computer Science article available to read on EtoBox.

What is Inverse Reinforcement Learning in Tracking Control Based on Inverse Optimal Control about?

This article provides a novel inverse reinforcement learning (RL) algorithm that learns an unknown performance objective function for tracking control. The algorithm combines three steps: 1) an optimal control update; 2) a gradient descent correction step; and 3) an inverse optimal control (IOC) update. The new algorithm clarifies the relation between inverse RL and IOC. It is shown that the reward weight of an unknown performance objective that generates a target control policy may not be unique. We characterize the set of all weights that generate the same target control policy. We develop a model-based algorithm and, further, two model-free algorithms for systems with unknown model information. Finally, simulation experiments are presented to show the effectiveness of the proposed algorithms.

Who reads Inverse Reinforcement Learning in Tracking Control Based on Inverse Optimal Control?

Author

Wenqian Xue; Patrik Kolaric; Jialu Fan; Bosen Lian; Tianyou Chai; Frank L. Lewis

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)