基于群体熵的机器人群体智能汇聚度量

doi:10.11959/j.issn.2096-6652.202213

Abstract

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.

Key words: swarm entropy, swarm intelligence, deep reinforcement learning

CLC Number:

TP181

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.

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[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	DORIGO M , BLUM C . Ant colony optimization theory:a survey[J]. Theoretical Computer Science, 2005,344(2/3): 243-278.
[6]	KR?SE B J A . Learning from delayed rewards[J]. Robotics and Autonomous Systems, 1995,15(4): 233-235.
[7]	SCHULMAN J , WOLSKI F , DHARIWAL P ,et al. Proximal policy optimization algorithms[J]. arXiv preprint, 2017,arXiv:1707.06347.
[8]	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.
[9]	SHANNON C E . A mathematical theory of communication[J]. The Bell System Technical Journal, 1948,27(3): 379-423.
[10]	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.
[11]	KAUFFMAN S A . The origins of order:self-organization and selection in evolution[M]. New York: Oxford University Press, 1993.
[12]	CORNEEL C . Emergent collective motion from local interactions[D]. Ghent:Ghent University, 2017.
[13]	FRANCESCHINI V , GIBERTI C , ZHENG Z M . Characterization of the Lorentz attractor by unstable periodic orbits[J]. Nonlinearity, 1993,6(2): 251-258.
[14]	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.
[15]	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.
[16]	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.
[17]	LOWE R , WU Y , TAMAR A ,et al. Multi-agent actor-critic for mixed cooperative-competitive environments[J]. arXiv preprint, 2017,arXiv:1706.02275.
[18]	SUNEHAG P , LEVER G , GRUSLYS A ,et al. Value-decomposition networks for cooperative multi-agent learning[J]. arXiv preprint, 2017,arXiv:1706.05296.
[19]	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.
[20]	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.
[21]	MNIH V , KAVUKCUOGLU K , SILVER D ,et al. Playing atari with deep reinforcement learning[J]. Computer Science, 2013.
[22]	RIPLEY B D , ROSENKRANTZ R D E T . Jaynes:papers on probability,statistics and statistical physics[J]. The Statistician, 1990,39(1): 90.

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