Deep reinforcement learning with emergent communication for coalitional negotiation games

DOI10.3934/mbe.2022212zbMath1501.91012OpenAlexW4224286468MaRDI QIDQ2086834

Yanyan Li

Publication date: 26 October 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2022212

zbMATH Keywords

cooperative gamesreinforcement learningmulti-agent systemsdeep learningemergent communication

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Cooperative games (91A12) Experimental studies (91A90)

Uses Software

• DeepStack
• SPMGBA

Cites Work

• Methods for task allocation via agent coalition formation
• Negotiating team formation using deep reinforcement learning
• Multiagent Systems
• Perfect Equilibrium in a Bargaining Model
• DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

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