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Schmid, Kyrill, Belzner, Lenz, Phan, Thomy, Gabor, Thomas and Linnhoff-Popien, Claudia (2020): Multi-agent Reinforcement Learning for Bargaining under Risk and Asymmetric Information. In: Icaart: Proceedings of the 12Th International Conference on Agents and Artificial Intelligence, Vol 1: pp. 144-151

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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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