基于群体熵的机器人群体智能汇聚度量

冯埔; 吴文峻; 罗杰; 于鑫; 田雍恺

doi:10.11959/j.issn.2096-6652.202213

您当前的位置：

首页 >

文章列表页 >

基于群体熵的机器人群体智能汇聚度量

专题：群体智能 | 更新时间：2024-06-05

- 基于群体熵的机器人群体智能汇聚度量
- Emergence measurement of robot swarm intelligence based on swarm entropy
- 智能科学与技术学报 2022年4卷第1期页码：65-74
- 作者机构：
  
  1. 北京航空航天大学计算机学院，北京 100191
  2. 北京航空航天大学人工智能研究院，北京 100191
- 作者简介：
  
  [ "冯埔（1995– ），男，北京航空航天大学计算机学院博士生，主要研究方向为多智能体强化学习" ]
  [ "吴文峻（1973– ），男，博士，北京航空航天大学人工智能研究院教授、博士生导师，主要研究方向为群体智能、智能微服务、认知建模等" ]
  [ "罗杰（1981– ），男，博士，北京航空航天大学计算机学院副教授，主要研究方向为知识图谱构、群体智能等" ]
  [ "于鑫（1994– ），男，北京航空航天大学计算机学院博士生，主要研究方向为群体智能、强化学习" ]
  [ "田雍恺（1997– ），男，北京航空航天大学计算机学院硕士生，主要研究方向为多智能体强化学习" ]
- 基金信息：
  
  科技创新2030—“新一代人工智能”重大项目;Science and Technology Innovation 2030 — “New Generation Artificial Intelligence” Major Project(2018AAA0102300)
- DOI：10.11959/j.issn.2096-6652.202213
  中图分类号： TP181
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-15
- 稿件说明：
移动端阅览
冯埔, 吴文峻, 罗杰, 等. 基于群体熵的机器人群体智能汇聚度量[J]. 智能科学与技术学报, 2022,4(1):65-74.

Pu FENG, Wenjun WU, Jie LUO, et al. Emergence measurement of robot swarm intelligence based on swarm entropy[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 65-74.
冯埔, 吴文峻, 罗杰, 等. 基于群体熵的机器人群体智能汇聚度量[J]. 智能科学与技术学报, 2022,4(1):65-74. DOI： 10.11959/j.issn.2096-6652.202213.

Pu FENG, Wenjun WU, Jie LUO, et al. Emergence measurement of robot swarm intelligence based on swarm entropy[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 65-74. DOI： 10.11959/j.issn.2096-6652.202213.

摘要

群体行为往往能产生远超个体行为的价值和复杂度。为了在个体智能的基础上更有效地衍生出群体智能，需要基于群体熵来科学地衡量群体智能水平，并以群体熵为引导目标，推动群体智能的增强和演进。针对这个重要的科学问题，以无人小车群体为研究对象，提出基于参数共享和群体策略熵的多智能体soft Q learning算法，通过共享智能体的观测信息，并结合最大熵强化学习方法，实现探索型任务中群体策略的持续学习更新。同时，通过将群体熵定义为度量工具，刻画群体学习中熵变化模式，实现对群智汇聚过程的定量分析。

Abstract

Swarm behavior can often produce value and complexity far beyond individual behavior.In order to more effectively derive swarm intelligence on the basis of individual intelligence

it is necessary to scientifically measure the level of swarm intelligence based on swarm entropy

and use swarm entropy as the guiding goal to promote the enhancement and evolution of swarm intelligence.Aiming at this important scientific problem

the unmanned car group as the research object was taken and a multi-agent soft Q learning method based on parameter sharing and group strategy entropy was proposed.Which by sharing the observation information of the agent

combined with the maximum entropy reinforcement learning method

to achieve continuous learning and updating of swarm strategies in exploratory tasks.At the same time

by defining swarm entropy as a measurement tool

characterizing the entropy change pattern in swarm learning

realizing the quantitative analysis of the gathering process of swarm intelligence.

关键词

Keywords

references

ZHU Y F , TANG X M . Overview of swarm intelligence [C ] // Proceedings of 2010 International Conference on Computer Application and System Modeling (ICCASM 2010) . Piscataway:IEEE Press , 2010 .

REYNOLDS C W , . Flocks,herds and schools:a distributed behavioral model [C ] // Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques . New York:ACM Press , 1987 : 25 - 34 .

VICSEK T , CZIRÓK A , BEN-JACOB E , et al . Novel type of phase transition in a system of self-driven particles [J ] . Physical Review Letters , 1995 , 75 ( 6 ): 1226 - 1229 .

DEB K , PRATAP A , AGARWAL S , et al . A fast and elitist multiobjective genetic algorithm:NSGA-II [J ] . IEEE Transactions on Evolutionary Computation , 2002 , 6 ( 2 ): 182 - 197 .

DORIGO M , BLUM C . Ant colony optimization theory:a survey [J ] . Theoretical Computer Science , 2005 , 344 ( 2/3 ): 243 - 278 .

KRÖSE B J A . Learning from delayed rewards [J ] . Robotics and Autonomous Systems , 1995 , 15 ( 4 ): 233 - 235 .

SCHULMAN J , WOLSKI F , DHARIWAL P , et al . Proximal policy optimization algorithms [J ] . arXiv preprint , 2017 ,arXiv:1707.06347.

FOERSTER J N , ASSAEL Y M , DE FREITAS N , et al . Learning to communicate with deep multi-agent reinforcement learning [J ] . Advances in Neural Information Processing Systems , 2016 ,29.

SHANNON C E . A mathematical theory of communication [J ] . The Bell System Technical Journal , 1948 , 27 ( 3 ): 379 - 423 .

李习彬 . 熵-信息理论与系统工程方法论的有效性分析 [J ] . 系统工程理论与实践 , 1994 , 14 ( 2 ): 37 - 42 .

LI X B . Entropy-information theory and an analysis of the effectiveness of systems engineering’s methodology [J ] . Systems Engineering Theory and Practice , 1994 , 14 ( 2 ): 37 - 42 .

KAUFFMAN S A . The origins of order:self-organization and selection in evolution [M ] . New York : Oxford University Press , 1993 .

CORNEEL C . Emergent collective motion from local interactions [D ] . Ghent:Ghent University , 2017 .

FRANCESCHINI V , GIBERTI C , ZHENG Z M . Characterization of the Lorentz attractor by unstable periodic orbits [J ] . Nonlinearity , 1993 , 6 ( 2 ): 251 - 258 .

罗杰 , 姜鑫 , 郭炳晖 , 等 . 群体智能系统的动力学模型与群体熵度量 [J ] . 中国科学:信息科学 , 2022 , 52 ( 1 ): 99 - 110 .

LUO J , JIANG X , GUO B H , et al . Dynamic model and crowd entropy measurement of crowd intelligence system [J ] . Scientia Sinica (Informationis) , 2022 , 52 ( 1 ): 99 - 110 .

于鑫 , 吴文峻 , 罗杰 , 等 . 面向群体共识机制的逆强化学习辨识方法 [J ] . 中国科学:技术科学 , 2021 .

YU X , WU W J , LUO J , et al . Inverse reinforcement learning identification method for group consensus mechanism [J ] . Scientia Sinica (Technologica) , 2021 .

YU X , WU W J , FENG P , et al . Swarm inverse reinforcement learning for biological systems [C ] // Proceedings of 2021 IEEE International Conference on Bioinformatics and Biomedicine . Piscataway:IEEE Press , 2021 : 274 - 279 .

LOWE R , WU Y , TAMAR A , et al . Multi-agent actor-critic for mixed cooperative-competitive environments [J ] . arXiv preprint , 2017 ,arXiv:1706.02275.

SUNEHAG P , LEVER G , GRUSLYS A , et al . Value-decomposition networks for cooperative multi-agent learning [J ] . arXiv preprint , 2017 ,arXiv:1706.05296.

RASHID T , SAMVELYAN M , DE WITT C S , et al . QMIX:monotonic value function factorisation for deep multi-agent reinforcement learning [J ] . arXiv preprint , 2018 ,arXiv:1803.11485.

HAARNOJA T , TANG H , ABBEEL P , et al . Reinforcement learning with deep energy-based policies [C ] // Proceedings of the International Conference on Machine Learning .[S.l.:s.n. ] , 2017 : 1352 - 1361 .

MNIH V , KAVUKCUOGLU K , SILVER D , et al . Playing atari with deep reinforcement learning [J ] . Computer Science , 2013 .

RIPLEY B D , ROSENKRANTZ R D E T . Jaynes:papers on probability,statistics and statistical physics [J ] . The Statistician , 1990 , 39 ( 1 ): 90 .

浏览量

599

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

深度强化学习应用于金融市场量化交易研究综述

面向数字货币量化交易的OAC模型研究

基于双池DQN的HVAC无模型优化控制方法

基于TD3的电动汽车复合电源能量管理策略研究

基于群体智能成果的路径规划程序自动生成系统