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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/12870

Title: Which types of learning make a simple game complex?
Authors: Hidaka, Shohei
Torii, Takuma
Masumi, Akira
Issue Date: 2015
Publisher: Complex Systems Publications
Magazine name: Complex Systems
Volume: 24
Number: 1
Start page: 49
End page: 74
Abstract: The present study focuses on a class of games with reinforcement-learning agents that adaptively choose their actions to locally maximize their rewards. By analyzing a limit model with a special type of learning, previous studies suggested that dynamics of games with learners may become chaotic. We evaluated the generality of this model by analyzing the consistency of this limit model in comparison with two other approaches, agent-based simulation and the Markov process model. Our analysis showed inconsistency between the limit model and two other models with more general reinforcement learning. This suggests that reinforcement learning does not lead to complex dynamics in games with learners.
Rights: Copyright (C) 2015 Complex Systems Publications. Shohei Hidaka, Takuma Torii, Akira Masumi, Complex Systems, 24(1), 2015, 49-74. This material is posted here with permission of Complex Systems Publications.
URI: http://hdl.handle.net/10119/12870
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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