首页|基于不完备信息预测的多智能体分布式协同

基于不完备信息预测的多智能体分布式协同

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  • 国家科技期刊平台
  • NETL
  • NSTL
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为了解决部分可观对抗环境中多智能体协同决策难题,受人大脑皮层通过记忆进行学习和推理功能启发,提出一种新的部分可观对抗环境下基于不完备信息预测的多智能体分布式协同决策框架.该框架可采用支持向量回归等多种预测方法通过历史记忆和当前观察信息对环境中不可见信息进行预测,并将预测信息和观察到的信息融合,作为协同决策的依据;再通过分布式多智能体强化学习进行协同策略学习得到团队中每个智能体的决策模型.使用该框架结合多种预测算法在典型的部分可观对抗环境中进行了多智能体协同决策的验证.结果表明,提出的框架对多种预测算法具有普适性,且在保证对不可见部分高预测精度时能将多智能体协同决策水平提升23.4％.
Multi-agent Distributed Cooperation Based on Incomplete Information Prediction
To solve the problem of multi-agent cooperative decision-making in a partially observable ad-versarial environment inspired by the learning and reasoning functions of the human cerebral cortex through memory,a new multi-agent distributed cooperative decision-making framework based on incomplete information prediction in a partially observable adversarial environment is proposed.The framework can use support vector regression and other prediction methods to predict invisible information in the environment through historical memory and current observed information and fuse the predicted information and the observed information as a basis of cooperative decision-making;Then,cooperative strategy learning is performed through distributed multi-agent reinforcement learning to obtain the decision-making model of each agent in the team.Multi-agent cooperative de-cision-making is verified in a typical partially observable adversarial environment using this framework and various prediction algorithms.The results show that the proposed framework is universal to va-rious prediction algorithms and can improve the level of multi-agent cooperative decision-making by 23.4％while ensuring high prediction accuracy for invisible parts.

multi-agent cooperationpartially observableinformation predictiondistributed cooperative deci-sion-makingadversarial environment

张宏达、李德才、何玉庆

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中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳 110016

中国科学院机器人与智能制造创新研究院,辽宁沈阳 110169

中国科学院大学,北京 100049

多智能体协同 部分可观 信息预测 分布式协同决策 对抗环境

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金辽宁省中央引导地方科技发展专项

619914139194820291848203919483032022JH6/100100009

2024

10.13976/j.cnki.xk.2023.2506

信息与控制
中国自动化学会 中国科学院沈阳自动化研究所

信息与控制

CSTPCD北大核心
影响因子：0.576
ISSN：1002-0411
年,卷(期)：2024.53(1)
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