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Updated: Jan 16, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Published on: October 14, 2017

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通过案例学习和好奇心实现协作多代理的双驱优化

Ruizhu Chen1, Rong Fei2, Junhuai Li2

  • 1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, shaanxi, China.

Neural networks : the official journal of the International Neural Network Society
|September 27, 2025
PubMed
概括

本研究引入了一种用于多代理深度强化学习 (MADRL) 的新方法,该方法可以改善勘探和开发. 案例增强的随机网络蒸探索 (CERE-CTDE) 范式提高了复杂场景中的学习效率和稳定性.

关键词:
基于案例的推理 基于案例的推理由好奇心驱动的好奇心驱动.勘探 - 开发权衡权衡.多代理的深度强化学习学习.星际飞船多代理挑战

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