Understanding the mechanism of human–computer game: a distributed reinforcement learning perspective

DOI10.1080/00207721.2020.1803436zbMath1483.91015OpenAlexW3049371293MaRDI QIDQ5026576

Yiyi Zhao, Jiangping Hu, Zhinan Peng, Bijoy Kumar Ghosh

Publication date: 8 February 2022

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2020.1803436

zbMATH Keywords

multi-agent systemreinforcement learningbipartite consensuscoopetition networkhuman-computer game

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Noncooperative games (91A10) Decision theory for games (91A35) Multi-agent systems (93A16)

Cites Work

• Unnamed Item
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• Emergent collective behaviors on coopetition networks
• Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm
• Multi-agent discrete-time graphical games and reinforcement learning solutions
• Human-computer negotiation in a three player market setting
• Bipartite output consensus in networked multi-agent systems of high-order power integrators with signed digraph and input noises
• Consensus Control of General Linear Multiagent Systems With Antagonistic Interactions and Communication Noises
• Consensus Problems on Networks With Antagonistic Interactions

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