作者：Emiland Garrabe Martina Lamberti Giovanni Russo
我们考虑了设计能够通过组合来自多个来源的数据来计算最优决策的代理的问题，以处理以下任务：（i）跟踪期望的行为，同时最小化特定于代理的成本；（ii）满足安全约束条件。在对控制问题进行公式化后，我们证明了在适当的假设下这是凸的，并找到了最优解。通过对真实车辆和驾驶员进行的计算机和体内实验，在联网汽车应用程序上说明了算法中的结果的有效性。所有实验都证实了我们的理论预测，该算法在实际车辆上的部署表明了其适用于车内操作。
We consider the problem of designing agents able to compute optimal decisions by composing data from multiple sources to tackle tasks involving: (i) tracking a desired behavior while minimizing an agent-specific cost; (ii) satisfying safety constraints. After formulating the control problem, we show that this is convex under a suitable assumption and find the optimal solution. The effectiveness of the results, which are turned in an algorithm, is illustrated on a connected cars application via in-silico and in-vivo experiments with real vehicles and drivers. All the experiments confirm our theoretical predictions and the deployment of the algorithm on a real vehicle shows its suitability for in-car operation.
论文链接：http://arxiv.org/pdf/2303.13315v1
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