Multi-agent Reinforcement Learning for Bargaining under Risk and Asymmetric Information

Kyrill Schmid, Lenz Belzner, Thomy Phan, Thomas Gabor, Claudia Linnhoff-Popien

Abstract

In cooperative game theory bargaining games refer to situations where players can agree to any one of a variety of outcomes but there is a conflict on which specific outcome to choose. However, the players cannot impose a specific outcome on others and if no agreement is reached all players receive a predetermined status quo outcome. Bargaining games have been studied from a variety of fields, including game theory, economics, psychology and simulation based methods like genetic algorithms. In this work we extend the analysis by means of deep multi-agent reinforcement learning (MARL). To study the dynamics of bargaining with reinforcement learning we propose two different bargaining environments which display the following situations: in the first domain two agents have to agree on the division of an asset, e.g., the division of a fixed amount of money between each other. The second domain models a seller-buyer scenario in which agents must agree on a price for a product. We empirically demonstrate that the bargaining result under MARL is influenced by agents’ risk-aversion as well as information asymmetry between agents.

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Paper Citation


in Harvard Style

Schmid K., Belzner L., Phan T., Gabor T. and Linnhoff-Popien C. (2020). Multi-agent Reinforcement Learning for Bargaining under Risk and Asymmetric Information.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-395-7, pages 144-151. DOI: 10.5220/0008913901440151


in Bibtex Style

@conference{icaart20,
author={Kyrill Schmid and Lenz Belzner and Thomy Phan and Thomas Gabor and Claudia Linnhoff-Popien},
title={Multi-agent Reinforcement Learning for Bargaining under Risk and Asymmetric Information},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2020},
pages={144-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008913901440151},
isbn={978-989-758-395-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Multi-agent Reinforcement Learning for Bargaining under Risk and Asymmetric Information
SN - 978-989-758-395-7
AU - Schmid K.
AU - Belzner L.
AU - Phan T.
AU - Gabor T.
AU - Linnhoff-Popien C.
PY - 2020
SP - 144
EP - 151
DO - 10.5220/0008913901440151