Stochastic Market Games

Stochastic Market Games

Kyrill Schmid, Lenz Belzner, Robert Müller, Johannes Tochtermann, Claudia Linnhoff-Popien

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 384-390. https://doi.org/10.24963/ijcai.2021/54

Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have conflicting goals. In these settings agents are likely to learn undesirable outcomes in terms of cooperation under independent learning, such as overly greedy behavior. Motivated from real world societies, in this work we propose to utilize market forces to provide incentives for agents to become cooperative. As demonstrated in an iterated version of the Prisoner's Dilemma, the proposed market formulation can change the dynamics of the game to consistently learn cooperative policies. Further we evaluate our approach in spatially and temporally extended settings for varying numbers of agents. We empirically find that the presence of markets can improve both the overall result and agent individual returns via their trading activities.
Keywords:
Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence
Agent-based and Multi-agent Systems: Coordination and Cooperation
Agent-based and Multi-agent Systems: Agent Societies