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International Journal of Control Volume 95, 2022 - Issue 9
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Research Article

Bellman's principle of optimality and deep reinforcement learning for time-varying tasks

Alessandro GiuseppiDepartment of Computer, Control, and Management Engineering, Antonio Ruberti at the University of Rome “La Sapeinza”, Rome, ItalyCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-5503-8506
ORCID Icon &
Antonio PietrabissaDepartment of Computer, Control, and Management Engineering, Antonio Ruberti at the University of Rome “La Sapeinza”, Rome, ItalyORCID Iconhttps://orcid.org/0000-0003-0188-3346
ORCID Icon
Pages 2448-2459 | Received 04 Oct 2020, Accepted 01 Apr 2021, Published online: 16 Apr 2021

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Raffaele Iervolino, Massimo Tipaldi & Ali Forootani. (2023) A Lyapunov-based version of the value iteration algorithm formulated as a discrete-time switched affine system. International Journal of Control 96:3, pages 577-592.
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Articles from other publishers (2)

Junjun Yang, Kaige Tan, Lei Feng, Ahmed M. El-Sherbeeny & Zhiwu Li. (2023) Reducing the Learning Time of Reinforcement Learning for the Supervisory Control of Discrete Event Systems. IEEE Access 11, pages 59840-59853.
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Xun Huang. (2022) Opponent cart-pole dynamics for reinforcement learning of competing agents面向竞争多智能体增强学习的对抗倒立摆动力学系统. Acta Mechanica Sinica 38:5.
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