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• W2885471572 abstract "This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory, either. Particles are devised with Q learning for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced experimental results are supportive to the proposed idea suggesting that a substantive collaboration can be build via proposed learning algorithm." @default.
• W2885471572 created "2018-08-22" @default.
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• W2885471572 date "2018-01-01" @default.
• W2885471572 modified "2023-09-24" @default.
• W2885471572 title "Building Collaboration in Multi-agent Systems Using Reinforcement Learning" @default.
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• W2885471572 doi "https://doi.org/10.1007/978-3-319-98446-9_19" @default.
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