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Updated: Jul 4, 2025

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
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Published on: August 2, 2018

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一个基于深度强化学习的关键参与者框架,用于解决灵活的工作车间安排问题的问题.

Cong Zhao1, Na Deng1

  • 1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

Mathematical biosciences and engineering : MBE
|February 2, 2024
PubMed
概括

本研究介绍了一种新的强化学习 (RL) 方法,用于灵活的工作车间安排问题 (FJSP). RL方法有效地优化了调度,在复杂的制造环境中优于传统方法.

关键词:
演员-评论家方法深度神经网络是一个神经网络.深度强化学习的学习.灵活的工作车间安排问题马尔科夫决策过程

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