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An adaptive-learning framework for semi-cooperative multi-agent coordination
2011
2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)
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In this research, we develop a general mathematical model for distributed, semi-cooperative planning and suggest a solution strategy which involves decomposing the system into subproblems, each of which ...
is specified at a certain period in time and controlled by an agent. ...
We develop a general, mathematical model of a multi-agent system for distributed, semi-cooperative planning, building on the DRTP modeling framework. 2.) ...
doi:10.1109/adprl.2011.5967386
dblp:conf/adprl/BoukhtoutaBPG11
fatcat:fbao2h32rrd3zleprs4qqdqp5q
Evolution of Cooperative Hunting in Artificial Multi-layered Societies
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2005.11580v2 fatcat:wepjal6qgneenjpkn4hqzytu2qOther Versions
In this paper, an agent-based model is proposed to study the evolution of cooperative hunting behaviors in an artificial society. ...
Experiments are carried out to test the evolution of cooperation in this closed-loop semi-supervised emergent system with different parameters. ...
In this paper, based on an evolutionary game theoretical framework, we extend it with multi-agent reinforcement learning to investigate the mechanisms behind the cooperative hunting phenomena in social ...
arXiv:2005.11580v5
fatcat:kcicqntdfzegjjg64zulxi5ylm
Human-Robot Teams in Entertainment and Other Everyday Scenarios
[article]
2009
arXiv
pre-print
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In this paper, we focus upon problem domains and tasks in which multiple robots, humans and other agents are cooperating through coordination to satisfy a set of goals or to maximize utility. ...
We discuss the teamwork problem and propose an architecture to address this. ...
Multi-agent planning coordinates the actions of multiple agents to achieve a goal [14] . ...
arXiv:0908.2661v1
fatcat:v5phwpiuf5ccddod6mbnovtpdi
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.13446v1 fatcat:kwim4gq73vflnernrrfdwhz34u
Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios. ...
In this paper, we introduce semi-on-policy (SOP) training as an effective and computationally efficient way to address the sample inefficiency of on-policy policy gradient methods. ...
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent
Safe and Efficient Off-Policy Reinforcement Learning. ...
arXiv:2104.13446v2
fatcat:aat2z6j7azcaxlqx5tnpwoxyje
Drone-Assisted Cellular Networks: A Multi-Agent Reinforcement Learning Approach
2019
ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
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Drone-assisted cellular networks: a multi-agent reinforcement learning approach. ...
We propose in this paper, a multiagent reinforcement learning approach for dynamic drones-cells management. Our approach is based on an enhanced joint action selection. ...
reinforcement learning model is based on a semi-centralized cooperative solution. ...
doi:10.1109/icc.2019.8762079
dblp:conf/icc/HammamiAMK19
fatcat:iu5jowqrrjgrph4iuioiogpfhu
Hierarchical multi-agent reinforcement learning
2006
Autonomous Agents and Multi-Agent Systems
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We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL. ...
We extend the multi-agent HRL framework to include communication decisions and propose a cooperative multi-agent HRL algorithm called COM-Cooperative HRL. ...
Acknowledgements The first author would like to thank Balaraman Ravindran for his useful comments. ...
doi:10.1007/s10458-006-7035-4
fatcat:7qyhm7mzfbgb5mkhwebz4oeehu
Hierarchical multi-agent reinforcement learning
2001
Proceedings of the fifth international conference on Autonomous agents - AGENTS '01
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We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL. ...
We extend the multi-agent HRL framework to include communication decisions and propose a cooperative multi-agent HRL algorithm called COM-Cooperative HRL. ...
Acknowledgements The first author would like to thank Balaraman Ravindran for his useful comments. ...
doi:10.1145/375735.376302
dblp:conf/agents/MakarMG01
fatcat:4s256e3aa5frvgbczyisddr5ja
A MULTI-AGENT REINFORCEMENT LEARNING FRAMEWORK FOR INTELLIGENT MANUFACTURING WITH AUTONOMOUS MOBILE ROBOTS
2021
Proceedings of the Design Society
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We specifically propose a multi-agent framework involving mobile robots, machines, humans. ...
This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. ...
MULTI-AGENT FRAMEWORK FOR MOBILE ROBOT DRIVEN SHOP FLOOR We propose a multi-agent framework for an autonomous mobile robot driven shop floor, wherein the sensory, control, and communication protocols have ...
doi:10.1017/pds.2021.17
fatcat:bjoy4jmoaffwvdundqzi3b4e5e
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
[article]
2020
arXiv
pre-print
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To realize this, we propose a Hierarchical Target-oriented Multi-Agent Coordination (HiT-MAC), which decomposes the target coverage problem into two-level tasks: targets assignment by a coordinator and ...
We also conduct an ablative analysis on the effectiveness of the introduced components in the framework. ...
Xiaotie Deng for their helpful discussion in our early work. ...
arXiv:2010.13110v1
fatcat:rlrqrjt76vaqpmebwrmn5oiyka
Multi-level Frontier based Topic-specific Crawler Design with Improved URL Ordering
2008
Computer and Information Science
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Coordinator agent is responsible for disseminating URLs from crawling frontiers to individual retrieval agents. ...
In this paper, a novel design of a topic specific web crawler based on multi-agent system is presented. The architecture proposed employs two types of agents: retrieval and coordinator agents. ...
In this paper, an architectural framework is presented for crawling topic specific Web pages using multi-agent based system. ...
doi:10.5539/cis.v1n4p99
fatcat:hafin6mrirb2nmfrl73ifm6wsu
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
[article]
2021
arXiv
pre-print
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In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and ...
We address these questions across foundational robotics domains, spanning perception, control, planning, and learning. ...
GNNs): While centralizedtraining, decentralized-execution (CTDE) [243] is the typical paradigm for multi-agent RL and multi-agent IL, the underlying machine learning framework can vary. ...
arXiv:2109.12343v1
fatcat:vxt62eluljfelcifzdlosv34cq
Evaluating semi-cooperative Nash/Stackelberg Q-learning for traffic routes plan in a single intersection
2020
Control Engineering Practice
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, with combining game theory and RL in decision-making in the multi-agent framework. ...
Then an extended version called semi-cooperative Stackelberg Q-learning is designed to make a comparison, where Nash equilibrium is replaced by Stackelberg equilibrium in the Q-learning process. ...
A multi-agent reinforcement learning coordination method can handle coordination problems in continuous action cooperative Markov games effectively (Zhang, Li, Hao, Chen, Tuyls et al., 2018) . ...
doi:10.1016/j.conengprac.2020.104525
fatcat:weu5ra2gvjanpkp3riqu6zrsxe
IJIMAI Editor�s Note - Vol. 2 Issue 4
2013
International Journal of Interactive Multimedia and Artificial Intelligence
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Results confirm the usefulness of the analysis tools when exporting to Cooperative Multi-agent Systems that use different configurations. ...
López et al. presents the progress and final state of CAIN-21, an extensible and metadata driven multimedia adaptation in the MPEG-21 framework. ...
doi:10.9781/ijimai.2013.240
fatcat:bvd67cuz4zbejg75b6tg4j5mwy
Understanding Behavior of System of Systems Through Computational Intelligence Techniques
2007
2007 1st Annual IEEE Systems Conference
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The semi-Therefore, the need to focus on overall system autonomous systems (people, organizations) are behavior is becoming an unavoidable issue. integrated through cooperative arrangements. ...
The world is facing an increasing the application area and focus [2], [11] , level of systems integration leading towards [14] .Future Combat Systems (FCS), NATO, trans-Systems of Systems (SoS) that adapt ...
Agents in memory. The long-term production memory Multi-agent systems (MAS) contain processes for coordinates all the modules in ACR-R [9] . ...
doi:10.1109/systems.2007.374658
fatcat:e7n524nxr5df5pnuied5ciagny
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
[chapter]
2019
Lecture Notes in Computer Science
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2019 pre-print arXiv:1910.09508v1 fatcat:gaxcxiydkvfy7nlhiep6fatztu
One obvious possibility is for each agent to broadcast their current intention, for example, the currently executed option in a hierarchical reinforcement learning framework. ...
We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours. ...
Method In this section we will present our framework for deep decentralized hierarchical multi-agent Q-learning. ...
doi:10.1007/978-3-030-29911-8_7
fatcat:uegmi357ynd7po47m25o5htybm
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