Adaptive temporal-difference learning via deep neural network function approximation: a non-asymptotic analysis

Adaptive temporal-difference learning via deep neural network function approximation: a non-asymptotic analysis

Abstract Although deep reinforcement learning has achieved notable practical achievements, its theoretical foundations have been scarcely explored until recent times. Nonetheless, the rate of convergence for current neural temporal-difference (TD) learning algorithms is constrained, largely due to t...

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Bibliographic Details
Main Authors: Guoyong Wang, Tiange Fu, Ruijuan Zheng, Xuhui Zhao, Junlong Zhu, Mingchuan Zhang
Format: Article
Language:English
Published: Springer 2025-01-01
Series:Complex & Intelligent Systems
Subjects:
Adaptive methods
Non-asymptotic convergence
Nonlinear function approximation
Reinforcement learning
Temporal-difference learning
Online Access:https://doi.org/10.1007/s40747-024-01757-w
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