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Learning Structured Communication for Multi-agent Reinforcement Learning
[article]
2020
arXiv
pre-print
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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
2020
Neural Information Processing Systems
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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
2021
Journal of Electronic Research and Application
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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
2021
Engineering, Technology & Applied Science Research
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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
2021
Tsinghua Science and Technology
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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]
2006
Lecture Notes in Computer Science
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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
2013
Sensors & Transducers
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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]
2020
arXiv
pre-print
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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
2020
Sensors
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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
2022
Entropy
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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
2009
2009 5th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities and Workshops
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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]
2011
Multi-Robot Systems, Trends and Development
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2010
International Journal of Sensor Networks (IJSNet)
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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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