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Learning Structured Communication for Multi-agent Reinforcement Learning [article]

Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, Hongyuan Zha
2020 arXiv   pre-print
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.  ...  Then we propose a novel framework termed as Learning Structured Communication (LSC) by using a more flexible and efficient communication topology.  ...  Conclusion and Future Work In this paper, a novel learning structured communication (LSC) algorithm has been proposed for multi-agent reinforcement learning.  ... 
arXiv:2002.04235v1 fatcat:th3tg5g3wbbubfbjcuwoixov2m

Learning Multi-Agent Communication through Structured Attentive Reasoning

Murtaza Rangwala, Ryan Williams
2020 Neural Information Processing Systems  
Learning communication via deep reinforcement learning has recently been shown to be an effective way to solve cooperative multi-agent tasks.  ...  By developing an explicit architecture that is targeted towards communication, our work aims to open new directions to overcome important challenges in multi-agent cooperation through learned communication  ...  Indeed, the ability to effectively represent and communicate information valuable to a task is especially important in multi-agent deep reinforcement learning (MADRL).  ... 
dblp:conf/nips/RangwalaW20 fatcat:wu5ly5gmirabbaralpzekhq32a

Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

Tianyun Qiu, Yaxuan Cheng
2021 Journal of Electronic Research and Application  
With the rapid advancement of deep reinforcement learning (DRL) in multi-agent systems, a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning  ...  Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently, and path planning for multiple robots using deep reinforcement learning is a new research  ...  Deep reinforcement learning-based path planning for multi-agent system.  ... 
doi:10.26689/jera.v5i6.2809 fatcat:ohkwlmyzlrdihpzxpwbufke6ni

Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

H. El Fazazi, M. Elgarej, M. Qbadou, K. Mansouri
2021 Engineering, Technology & Applied Science Research  
In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented.  ...  A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective.  ...  CONCLUSION In this paper, a design for an adaptive e-learning system based on a multi-agent approach and reinforcement learning has been proposed.  ... 
doi:10.48084/etasr.3905 fatcat:n4vy5awym5cy3hrkj5tk3u4yhu

Multi-agent modeling and simulation in the AI age

Wenhui Fan, Peiyu Chen, Daiming Shi, Xudong Guo, Li Kou
2021 Tsinghua Science and Technology  
Then we review the development status of the hybrid modeling and simulation combining multi-agent and system dynamics, the modeling and simulation of multi-agent reinforcement learning, and the modeling  ...  It also paves the way for further research on MAMS technology. Wenhui Fan et al.: Multi-Agent Modeling and Simulation in the AI Age 609 2 Multi-Agent Modeling and Simulation 2.  ...  of agents in multi-agent reinforcement learning. (3) The expansibility and knowledge transfer ability of multi-agent reinforcement learning are poor.  ... 
doi:10.26599/tst.2021.9010005 fatcat:em72oiw5mvgc7lp3pjmch3n2eq

Multi-agent Relational Reinforcement Learning [chapter]

Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice Bruynooghe
2006 Lecture Notes in Computer Science  
Yet, this relational structure has not been exploited for multi-agent reinforcement learning tasks and has only been studied in a single agent context so far.  ...  In this paper we report on using a relational state space in multi-agent reinforcement learning.  ...  To illustrate the need for these structural representations, we will describe the blocks world domain as a Reinforcement Learning problem.  ... 
doi:10.1007/11691839_12 fatcat:q6hfxggsinhjzk254b5crs3oxe

Wireless Sensor Network Topology Control Based on Agent

Chaoyu YANG, Yerong HE, Minli SONG
2013 Sensors & Transducers  
Based on multi-Agent and learning-reinforcement adaptive topology control algorithm of wireless sensor network, this paper abstracts the wireless sensor network topology control into multi-Agent and global-coordination  ...  This paper also probes into forming initial topological structure and data forwarding path by the interaction of detection information and return information among local Agents, and probes into ensuring  ...  The Collaboration with Multi-Agent Reinforcement Learning In the machine learning, Agent Reinforcement Learning aims to enable the Agent sense the environment by learning to choose the optimal action for  ... 
doaj:46d2d75ee0e9471ab58fbc4432d93abb fatcat:2zkulxnks5aofbe6jnquxzdewi

Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning [article]

Paul Van Eecke
2020 arXiv   pre-print
In this paper, we formulate the challenge of re-conceptualising the language game experimental paradigm in the framework of multi-agent reinforcement learning (MARL).  ...  If successful, future language game experiments will benefit from the rapid and promising methodological advances in the MARL community, while future MARL experiments on learning emergent communication  ...  Conclusion and Outlook Multi-agent reinforcement learning forms a natural framework for conducting experiments on learning emergent communication, and has been adopted as a methodology of choice in many  ... 
arXiv:2004.04722v1 fatcat:qas56pic7vdbnivoz2243auh4q

An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Qiang Wu, Jianqing Wu, Jun Shen, Binbin Yong, Qingguo Zhou
2020 Sensors  
In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an  ...  The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control  ...  Figure 2 . 2 The multi-agent reinforcement learning (MARL) structure in urban traffic signal control.  ... 
doi:10.3390/s20154291 pmid:32752055 pmcid:PMC7436084 fatcat:czp6qn2mpncchfco2sjohm76ny

Optimal Policy of Multiplayer Poker via Actor-Critic Reinforcement Learning

Daming Shi, Xudong Guo, Yi Liu, Wenhui Fan
2022 Entropy  
This paper proposes an optimal policy learning method for multi-player poker games based on Actor-Critic reinforcement learning.  ...  Secondly, this paper proposes a novel multi-player poker policy update method: asynchronous policy update algorithm (APU) and dual-network asynchronous policy update algorithm (Dual-APU) for multi-player  ...  of multi-agent reinforcement learning; thirdly, Section 4 clarifies the optimal policy learning method based on Actor-Critic reinforcement learning, the network structure for poker learning tasks, and  ... 
doi:10.3390/e24060774 pmid:35741495 pmcid:PMC9222241 fatcat:4i3ubapckfg6pjl4iz5jfdtweu

A novel cooperative communication protocol for QoS provisioning in wireless sensor networks

Xuedong Liang, Min Chen, Yang Xiao, Ilangko Balasingham, Victor C.M. Leung
2009 2009 5th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities and Workshops  
Learning based multi-hop mesh Cooperative Communication mechanism for wireless sensor networks.  ...  In this paper, we investigate the use of cooperative communications for quality of service (QoS) provisioning in resource-constrained wireless sensor networks, and propose MRL-CC, a Multi-agent Reinforcement  ...  reinforcement learning based multi-hop mesh cooperative communication mechanism for wireless sensor networks.  ... 
doi:10.1109/tridentcom.2009.4976244 dblp:conf/tridentcom/LiangCXBL09 fatcat:f6sxr2t6mfaghmhcgdvqiu4kf4

Multi-Robot Information Fusion and Coordination Based on Agent [chapter]

Bo Fan, Jiexin Pu
2011 Multi-Robot Systems, Trends and Development  
Multi-agent coordination based on reinforcement learning In this section, the multi-agent coordination based on distributed reinforcement learning is proposed, which is shown in Figure 11 .  ...  Applied to multi-agent system, reinforcement learning is extended to Markov games.  ...  Summary In multi-agent environment, neglecting the agents' interaction of competition and cooperation, multi-agent learning can not acquire the better performance.  ... 
doi:10.5772/13029 fatcat:yg3uyhyb5vc7fb3jq7tzboyoh4

HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging [article]

Nikunj Gupta, G Srinivasaraghavan, Swarup Kumar Mohalik, Matthew E. Taylor
2021 arXiv   pre-print
Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation learning abilities of deep neural networks.  ...  Empirical results show that 1) learned communication does indeed improve system performance, 2) results generalize to multiple numbers of agents, and 3) results generalize to different reward structures  ...  We also would like to thank Shahil Mawjee and anonymous reviewers for comments and suggestions on earlier versions of this paper.  ... 
arXiv:2102.00824v1 fatcat:u3deetdxwvh6vffrqvueqto2xa

Open Challenges and Issues: Artificial Intelligence for Transactive Management [article]

Asma Khatun, Sk. Golam Sarowar Hossain
2020 arXiv   pre-print
The aim of this article is to look for the current development of TM methods based on AI and Machine Learning (ML) technology.  ...  This paper also finds that MAS based method faces major difficulty to design or set up goal to various agents and describes how ML technique can contribute to that solution.  ...  However, these learning techniques are not well suited for the multi agent as these are type of aimless learning (Khalil, 2015) .  ... 
arXiv:2001.03238v1 fatcat:ze2z5nnuxvfyvli555vdkbdzl4

MRL-CC: a novel cooperative communication protocol for QoS provisioning in wireless sensor networks

Xuedong Liang, Min Chen, Yang Xiao, Ilangko Balasingham, Victor C.M. Leung
2010 International Journal of Sensor Networks (IJSNet)  
In this paper, we investigate the use of cooperative communications for quality of service (QoS) provisioning in resource-constrained wireless sensor networks, and propose MRL-CC, a Multi-agent Reinforcement  ...  Learning based multi-hop mesh Cooperative Communication mechanism.  ...  Acknowledgment This research is in the context of the EU project IST-33826 CREDO: Modeling and analysis of evolutionary structures for distributed services (http://www.cwi.nl/projects/credo/).  ... 
doi:10.1504/ijsnet.2010.034619 fatcat:jzlqnjnj6rec7c7rgko73xtkki
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