[1]郭 鑫,王 微,青 伟,等.基于强化学习的多智能体泛化性研究[J].计算机技术与发展,2023,33(04):114-119.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 017]
　GUO Xin,WANG Wei,QING Wei,et al.Research on Generalization of Multi-agent Based on Reinforcement Learning[J].,2023,33(04):114-119.[doi:10. 3969 / j. issn. 1673-629X. 2023. 04. 017]

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基于强化学习的多智能体泛化性研究()

分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

33

期数:

2023年04期

页码:

114-119

栏目:

人工智能

出版日期:

2023-04-10

文章信息/Info

Title:

Research on Generalization of Multi-agent Based on Reinforcement Learning

文章编号:

1673-629X(2023)04-0114-06

作者:

郭 鑫; 王 微; 青 伟; 李 剑; 何召锋

北京邮电大学,北京 100088

Author(s):

GUO Xin; WANG Wei; QING Wei; LI Jian; HE Zhao-feng

Beijing University of Posts and Telecommunications,Beijing 100088,China

关键词:

深度强化学习方法; 多智能体; 未知环境; 策略集成; 泛化性; 可扩展性

Keywords:

deep reinforcement learning; multi-agent; unknown environment; policy ensemble; generalization; scalability

分类号:

TP181

DOI:

10. 3969 / j. issn. 1673-629X. 2023. 04. 017

摘要:

在多智能体强化学习算法的研究中,由于训练与测试环境具有差异,如何让智能体有效地应对环境中其他智能体策略变化的情况受到研究人员的广泛关注。 针对这一泛化性问题,提出基于人类偏好的多智能体角色策略集成算法,该算法同时考虑了长期回报和即时回报。 这一改进使得智能体从一些具有良好长期累积回报的候选行动中选择具有最大即时回报的行动,从而让算法确定了策略更新的方向,避免过度探索和无效训练,能快速找到最优策略。 此外,智能体被动态地划分为不同的角色,同角色智能体共享参数,不仅提高了效率,而且实现了多智能体算法的可扩展性。 在多智能体粒子环境中与现有算法的比较表明,该算法的智能体能更好地泛化到未知环境,且收敛速度更快,能够更高效地训练出最优策略。

Abstract:

In the research of multi-agent reinforcement learning algorithm,due to the difference between training and?

testing environment,how to make agents intelligently learn to cope with the performance degradation caused?

by the change of other agents’policy in the environment has been widely concerned by researchers. To solve?

this generalization problem, human - preference based multi - agent rolepolicy ensemble is proposed, which considers the effects of long - term reward and immediate reward. This improvement enables thealgorithm to determine the direction of policy updating to avoid excessive exploration and ineffective training. In addition, agents areclassified into different roles according to their immediate rewards of historical actions. Thus the parameters are shared with the same -role agent,which improves efficiency and achieves the scalability of the multi - agent algorithm. The comparison with the existingalgorithm in the multi-agent particle environment shows that the proposed algorithm has a faster convergence speed which can effectivelytrain the optimal strategy,and its intelligence can better generalize to the unknown environment.

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