We consider a cooperative multi-agent system consisting of a team of agents with decentralized information. Our focus is on the design of symmetric (i.e. identical) strategies for the agents in order to optimize a finite horizon team objective. We start with a general information structure and then consider some special cases. The constraint of using symmetric strategies introduces new features and complications in the team problem. For example, we show in a simple example that randomized symmetric strategies may outperform deterministic symmetric strategies. We also discuss why some of the known approaches for reducing agents' private information in teams may not work under the constraint of symmetric strategies. We then adopt the common information approach for our problem and modify it to accommodate the use of symmetric strategies. This results in a common information based dynamic program where each step involves minimization over a single function from the space of an agent's private information to the space of probability distributions over actions. We present specialized models where private information can be reduced using simple dynamic program based arguments.

翻译：我们考虑一个由具有分散信息的智能体团队组成的合作多智能体系统。我们关注的重点是为智能体设计对称（即相同）策略，以优化有限时域团队目标。我们从一般信息结构出发，然后考察一些特殊情况。使用对称策略的约束在团队问题中引入了新的特性和复杂性。例如，我们通过一个简单示例表明，随机对称策略可能优于确定性对称策略。我们还讨论了在对称策略约束下，某些用于减少团队中智能体私有信息的已知方法为何可能失效。随后，我们针对问题采用共同信息方法，并对其进行调整以适应对称策略的使用。这产生了一个基于共同信息的动态规划，其中每一步都涉及在从智能体私有信息空间到行动概率分布空间上的单个函数的极小化。我们给出了专用模型，在这些模型中，可以通过基于动态规划的简单论证来减少私有信息。