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Multi-agent reinforcement learning for bargaining under risk and asymmetric information

  • 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 empirica lly demonstrate that the bargaining result underIn 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 empirica lly demonstrate that the bargaining result under MARL is influenced by agents’ risk-aversion as well as information asymmetry between agents.show moreshow less

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Metadaten
Author:Kyrill Schmid, Lenz BelznerORCiD, Thomy Phan, Thomas Gabor, Claudia Linnhoff-Popien
Language:English
Document Type:Conference Paper
Conference:12th International Conference on Agents and Artificial Intelligence, Valletta (Malta), 22.-24.02.2020
Year of first Publication:2020
published in (English):Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART 2020
Editor(s):Ana Rocha, Luc Steels, Jaap van den Herik
Publisher:SciTePress
Place of publication:Setúbal
ISBN:978-989-758-395-7
ISSN:2184-433X
First Page:144
Last Page:151
Review:peer-review
Open Access:ja
Related Identifier:https://doi.org/10.5220/0008913901440151
Licence (German):License Logo Creative Commons BY-NC-ND 4.0
Release Date:2022/03/14