@inproceedings{SchmidBelznerPhanetal.2020, author = {Schmid, Kyrill and Belzner, Lenz and Phan, Thomy and Gabor, Thomas and Linnhoff-Popien, Claudia}, 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 2020}, editor = {Rocha, Ana and Steels, Luc and Herik, Jaap van den}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-395-7}, issn = {2184-433X}, doi = {https://doi.org/10.5220/0008913901440151}, pages = {144 -- 151}, year = {2020}, 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 empirica lly demonstrate that the bargaining result under MARL is influenced by agents' risk-aversion as well as information asymmetry between agents.}, language = {en} }