Relevant bibliographies by topics / Multi-Agent Q-Learning

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  1. Journal articles

Academic literature on the topic 'Multi-Agent Q-Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Multi-Agent Q-Learning"

1

Hwang, Kao-Shing, Wei-Cheng Jiang, Yu-Hong Lin, and Li-Hsin Lai. "CONTINUOUS Q-LEARNING FOR MULTI-AGENT COOPERATION." Cybernetics and Systems 43, no. 3 (2012): 227–56. http://dx.doi.org/10.1080/01969722.2012.660032.

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2

Galstyan, Aram. "Continuous strategy replicator dynamics for multi-agent Q-learning." Autonomous Agents and Multi-Agent Systems 26, no. 1 (2011): 37–53. http://dx.doi.org/10.1007/s10458-011-9181-6.

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3

ICHIKAWA, Yoshihiro, and Keiki TAKADAMA. "Conflict Avoidance for Multi-agent Q-learning Based on Learning Progress." Transactions of the Society of Instrument and Control Engineers 48, no. 11 (2012): 764–72. http://dx.doi.org/10.9746/sicetr.48.764.

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4

Xiao, Yuchen, Joshua Hoffman, Tian Xia, and Christopher Amato. "Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13965–66. http://dx.doi.org/10.1609/aaai.v34i10.7255.

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We consider the challenges of learning multi-agent/robot macro-action-based deep Q-nets including how to properly update each macro-action value and accurately maintain macro-action-observation trajectories. We address these challenges by first proposing two fundamental frameworks for learning macro-action-value function and joint macro-action-value function. Furthermore, we present two new approaches of learning decentralized macro-action-based policies, which involve a new double Q-update rule that facilitates the learning of decentralized Q-nets by using a centralized Q-net for action selec
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5

Ge, Yangyang, Fei Zhu, Wei Huang, Peiyao Zhao, and Quan Liu. "Multi-agent cooperation Q-learning algorithm based on constrained Markov Game." Computer Science and Information Systems 17, no. 2 (2020): 647–64. http://dx.doi.org/10.2298/csis191220009g.

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Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in th
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6

Matta, M., G. C. Cardarilli, L. Di Nunzio, et al. "Q‐RTS: a real‐time swarm intelligence based on multi‐agent Q‐learning." Electronics Letters 55, no. 10 (2019): 589–91. http://dx.doi.org/10.1049/el.2019.0244.

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7

Matignon, Laetitia, Guillaume J. Laurent, and Nadine Le Fort-Piat. "Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems." Knowledge Engineering Review 27, no. 1 (2012): 1–31. http://dx.doi.org/10.1017/s0269888912000057.

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AbstractIn the framework of fully cooperative multi-agent systems, independent (non-communicative) agents that learn by reinforcement must overcome several difficulties to manage to coordinate. This paper identifies several challenges responsible for the non-coordination of independent agents: Pareto-selection, non-stationarity, stochasticity, alter-exploration and shadowed equilibria. A selection of multi-agent domains is classified according to those challenges: matrix games, Boutilier's coordination game, predators pursuit domains and a special multi-state game. Moreover, the performance of
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8

Park, Kui-Hong, Yong-Jae Kim, and Jong-Hwan Kim. "Modular Q-learning based multi-agent cooperation for robot soccer." Robotics and Autonomous Systems 35, no. 2 (2001): 109–22. http://dx.doi.org/10.1016/s0921-8890(01)00114-2.

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9

Yin, Xijie, and Dongxin Yang. "Q Value Reinforcement Learning Algorithm Based on Multi Agent System." Journal of Physics: Conference Series 1069 (August 2018): 012094. http://dx.doi.org/10.1088/1742-6596/1069/1/012094.

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10

Hwang, Kao-Shing, Yu-Jen Chen, Wei-Cheng Jiang, and Tzung-Feng Lin. "Continuous Action Generation of Q-Learning in Multi-Agent Cooperation." Asian Journal of Control 15, no. 4 (2012): 1011–20. http://dx.doi.org/10.1002/asjc.614.

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