Trust-region inverse reinforcement learning

This paper proposes a new unified inverse reinforcement learning (IRL) framework based on trust-region methods and a recently proposed Pontryagin differential programming (PDP) method in Jin et al. (2020), which aims to learn the parameters in both the system model and the cost function for three ty...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Cao, Kun, Xie, Lihua
مؤلفون آخرون: School of Electrical and Electronic Engineering
التنسيق: مقال
اللغة:English
منشور في: 2023
الموضوعات:
PMP
الوصول للمادة أونلاين:https://hdl.handle.net/10356/170705
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
id sg-ntu-dr.10356-170705
record_format dspace
spelling sg-ntu-dr.10356-1707052023-09-26T05:54:28Z Trust-region inverse reinforcement learning Cao, Kun Xie, Lihua School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Trust Region Methods PMP This paper proposes a new unified inverse reinforcement learning (IRL) framework based on trust-region methods and a recently proposed Pontryagin differential programming (PDP) method in Jin et al. (2020), which aims to learn the parameters in both the system model and the cost function for three types of problems, namely, N-player nonzero-sum multistage games, 2-player zero-sum multistage games and 1-player optimal control, from demonstrated trajectories. Different from the existing frameworks using gradient to update learning parameters, our framework updates them with the candidate solution of trust-region subproblem (TRS), where its required gradient and Hessian are obtained by differentiating Pontryagin's Maximum Principle (PMP) equations once and twice, respectively. The differentiated equations are shown to be equivalent to the PMP equations for affine-quadratic games / optimal control problems and can be solved by some explicit recursions. Extensive simulation examples and comparisons are presented to demonstrate the effectiveness of our proposed algorithm. Nanyang Technological University This work was supported by the Wallenberg-NTU Presidential Postdoctoral Fellowship in Nanyang Technological University, Singapore. 2023-09-26T05:54:28Z 2023-09-26T05:54:28Z 2023 Journal Article Cao, K. & Xie, L. (2023). Trust-region inverse reinforcement learning. IEEE Transactions On Automatic Control, 1-8. https://dx.doi.org/10.1109/TAC.2023.3274629 0018-9286 https://hdl.handle.net/10356/170705 10.1109/TAC.2023.3274629 2-s2.0-85159816020 1 8 en IEEE Transactions on Automatic Control © 2023 IEEE. All right reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Trust Region Methods
PMP
spellingShingle Engineering::Electrical and electronic engineering
Trust Region Methods
PMP
Cao, Kun
Xie, Lihua
Trust-region inverse reinforcement learning
description This paper proposes a new unified inverse reinforcement learning (IRL) framework based on trust-region methods and a recently proposed Pontryagin differential programming (PDP) method in Jin et al. (2020), which aims to learn the parameters in both the system model and the cost function for three types of problems, namely, N-player nonzero-sum multistage games, 2-player zero-sum multistage games and 1-player optimal control, from demonstrated trajectories. Different from the existing frameworks using gradient to update learning parameters, our framework updates them with the candidate solution of trust-region subproblem (TRS), where its required gradient and Hessian are obtained by differentiating Pontryagin's Maximum Principle (PMP) equations once and twice, respectively. The differentiated equations are shown to be equivalent to the PMP equations for affine-quadratic games / optimal control problems and can be solved by some explicit recursions. Extensive simulation examples and comparisons are presented to demonstrate the effectiveness of our proposed algorithm.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Cao, Kun
Xie, Lihua
format Article
author Cao, Kun
Xie, Lihua
author_sort Cao, Kun
title Trust-region inverse reinforcement learning
title_short Trust-region inverse reinforcement learning
title_full Trust-region inverse reinforcement learning
title_fullStr Trust-region inverse reinforcement learning
title_full_unstemmed Trust-region inverse reinforcement learning
title_sort trust-region inverse reinforcement learning
publishDate 2023
url https://hdl.handle.net/10356/170705
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