The overall framework of the TPDEB.

<div><p>To address the inefficiencies in sample utilization and policy instability in asynchronous distributed reinforcement learning, we propose TPDEB—a dual experience replay framework that integrates prioritized sampling and temporal diversity. While recent distributed RL systems have...

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Bibliographic Details
Main Author:	Teh Noranis Mohd Aris (22600931) (author)
Other Authors:	Ningning Chen (509273) (author), Norwati Mustapha (17029699) (author), Maslina Zolkepli (22600934) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Sociology Science Policy Biological Sciences not elsewhere classified two key mechanisms prioritized replay buffers enables better trade ablation studies validate asynchronous actor updates integrates prioritized sampling findings support tpdeb single experience buffer improving learning robustness redundant experience buffer strategy asynchronous conditions unbiased sampling robust learning regularized learning inefficient sampling tpdeb employs tpdeb collects tpdeb addresses xlink "> wise methods temporal diversity scaled well scalable solution sample utilization quality samples often suffer learner bandwidth induced delays final performance empirical evaluations driven prioritization convergence speed combines standard
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The overall framework of the TPDEB.

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