Learning competitive pricing strategies by multi-agent reinforcement learning

DOI10.1016/S0165-1889(02)00122-7zbMath1179.91194MaRDI QIDQ951423

Thomas Uthmann, Daniel Polani, Erich Kutschinski

Publication date: 24 October 2008

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

zbMATH Keywords

dynamic pricingagent-based computational economicsQ-learningdistributed simulationmulti-agent reinforcement learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Microeconomic theory (price theory and economic markets) (91B24) Economic models of real-world systems (e.g., electricity markets, etc.) (91B74) Agent technology and artificial intelligence (68T42)

Related Items (5)

FROM CHOICE OF PROCUREMENT STRATEGY TO SUPPLY NETWORK CONFIGURATION: AN EVOLUTIONARY APPROACH ⋮ Automated repricing and ordering strategies in competitive markets ⋮ Integrating real and financial markets in an agent-based economic model: An application to monetary policy design ⋮ Single-leader-multiple-follower games with boundedly rational agents ⋮ Particle swarm optimization algorithm for agent-based artificial markets

Cites Work

• \({\mathcal Q}\)-learning

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