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.

本文提出了利用市场力量鼓励多智能体系统中的协作行为，以应对智能驾驶或者工厂作为服务的具有相互冲突目标的混合动机场景。作者在包括囚徒困境博弈等迭代环节中证明了他们提出的市场推荐机制可以持续地学习协作策略，并证明在不同的智能体数量上，在时间和空间上的考验下，利用市场力量可以提高整体效果和智能体单个回报。

随机市场博弈