T Neural Networks and Learning Systems
The IEEE Transactions on Neural Networks and Learning Systems publishes technical articles that deal with the theory, design, and applications of neural networks and related learning systems.
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IEEE TNNLS Special Issue on "Stream Learning," Guest Editors: Jie Lu, University of Technology Sydney, Australia; Joao Gama, University of Porto, Portugal; Xin Yao, Southern University of Science and Technology, China; Leandro Minku, University of Birmingham, UK. Submission Deadline: November 1, 2021. [Call for Papers]
IEEE TNNLS Special Issue on "Causal Discovery and Causality-Inspired Machine Learning," Guest Editors: Kun Zhang, Carnegie Mellon University, USA; Ilya Shpitser, John Hopkins University, USA; Sara Magliacane, University of Amsterdam, Netherlands; Davide Bacciu, University of Pisa, Italy; Fei Wu, Zhejiang University, China; Changshui Zhang, Tsinghua University, Chinal Peter Spirtes, Carnegie Mellon University, USA. Submission Deadline: October 22, 2021. [Call for Papers]
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IEEE TNNLS Special Issue on "Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications," Guest Editors: Ming Li, Zhejiang Normal University, China; Alessio Micheli, University of Pisa, Italy; Yu Guang Wang, Max Planck Institute for Mathematics in the Sciences, Germany; Shirui Pan, Monash University, Australia; Pietro Liò, University of Cambridge, UK; Giorgio Stefano Gnecco, IMT School for Advanced Studies, AXES Research Unit, Italy; Marcello Sanguineti, University of Genoa, Italy. Submission Deadline: July 31, 2021. [Call for Papers]
The Boundedness Conditions for Model-Free HDP( λ )
Authors: Seaar Al-Dabooni, Donald Wunsch
Publication: IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
Issue: Volume 30, Issue 7 – July 2019
Abstract: This paper provides the stability analysis for a model-free action-dependent heuristic dynamic programing (HDP) approach with an eligibility trace long-term prediction parameter (λ). HDP(λ) learns from more than one future reward. Eligibility traces have long been popular in Q-learning. This paper proves and demonstrates that they are worthwhile to use with HDP. In this paper, we prove its uniformly ultimately bounded (UUB) property under certain conditions. Previous works present a UUB proof for traditional HDP [HDP(λ = 0)], but we extend the proof with the λ parameter. By using Lyapunov stability, we demonstrate the boundedness of the estimated error for the critic and actor neural networks as well as learning rate parameters. Three case studies demonstrate the effectiveness of HDP(λ). The trajectories of the internal reinforcement signal nonlinear system are considered as the first case. We compare the results with the performance of HDP and traditional temporal difference [TD(λ)] with different λ values. The second case study is a single-link inverted pendulum. We investigate the performance of the inverted pendulum by comparing HDP(λ) with regular HDP, with different levels of noise. The third case study is a 3-D maze navigation benchmark, which is compared with state action reward state action, Q(λ), HDP, and HDP(λ). All these simulation results illustrate that HDP(λ) has a competitive performance; thus this contribution is not only UUB but also useful in comparison with traditional HDP.
Index Terms: λ-return, action dependent (AD), approximate dynamic programing (ADP), heuristic dynamic programing (HDP), Lyapunov stability, model free, uniformly ultimately bounded (UUB)
IEEE Xplore Link: https://ieeexplore.ieee.org/document/8528554