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.…
Author: | Kyrill Schmid, Lenz BelznerORCiD, Thomy Phan, Thomas Gabor, Claudia Linnhoff-Popien |
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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): | ![]() |
Release Date: | 2022/03/14 |