Technical Note: \cal Q-Learning
Machine Learning
Bargaining theory with applications
Bargaining theory with applications
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Human vs. Computer Behaviour in Multi-Issue Negotiation
RRS '05 Proceedings of the Rational, Robust, and Secure Negotiation Mechanisms in Multi-Agent Systems (RRS'05) on Multi-Agent Systems
Simulating human-like decisions in a memory-based agent model
Computational & Mathematical Organization Theory
Path selection in disaster response management based on Q-learning
International Journal of Automation and Computing
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This paper addresses agent modeling in multiagent-based simulation (MABS) to explore agents who can reproduce humanlike behaviors in the sequential bargaining game, which is more difficult to be reproduced than in the ultimate game (i.e., one time bargaining game). For this purpose, we focus on the Roth's learning agents who can reproduce human-like behaviors in several simple examples including the ultimate game, and compare simulation results of Roth's learning agents and Q-learning agents in the sequential bargaining game. Intensive simulations have revealed the following implications: (1) Roth's basic and three parameter reinforcement learning agents with any type of three action selections (i.e., Ɛ-greed, roulette, and Boltzmann distribution selections) can neither learn consistent behaviors nor acquire sequential negotiation in sequential bargaining game; and (2) Q-learning agents with any type of three action selections, on the other hand, can learn consistent behaviors and acquire sequential negotiation in the same game. However, Q-learning agents cannot reproduce the decreasing trend found in subject experiments.