Optimal Decision-Making for Autonomous Agents via Data Composition
Optimization and Control
2023-05-23 v2
Abstract
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.
Cite
@article{arxiv.2303.13315,
title = {Optimal Decision-Making for Autonomous Agents via Data Composition},
author = {Emiland Garrabe and Martina Lamberti and Giovanni Russo},
journal= {arXiv preprint arXiv:2303.13315},
year = {2023}
}