To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allow agents in the system to vote on which traffic signal controllers, which were trained on different goals using deep reinforcement learning, would control the intersection. Taking decisions based on suitable voting procedures turns out to lead to favorable solutions, which perform highly for several goals rather than optimally for one goal and poorly for others. This opens up new opportunities for the management or even self-governance of complex systems that require the consideration and achievement of multiple goals, such as many systems involving humans. Here, we present results for traffic flows in urban street networks, which suggest that “democratizing traffic” would be a promising alternative to centralized control of traffic flows.

中文翻译：

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智慧城市交通控制民主化

为了提高系统的性能，优化一直是过去流行的方法。然而，当需要同时实现多个目标时，该方法面临挑战。为了说明这一点，我们研究了一个多智能体系统，其中智能体具有多个不同且相互不一致的目标。然后，我们允许系统中的代理投票决定哪些交通信号控制器将控制十字路口，这些控制器使用深度强化学习针对不同目标进行了训练。根据适当的投票程序做出的决策最终会产生有利的解决方案，这些解决方案对多个目标表现良好，而不是对一个目标表现最佳，而对其他目标表现不佳。这为复杂系统的管理甚至自我治理开辟了新的机会，这些系统需要考虑和实现多个目标，例如许多涉及人类的系统。在这里，我们提出了城市街道网络交通流的结果，这表明“交通民主化”将是交通流集中控制的一个有前途的替代方案。