Probabilistic design of optimal sequential decision-making algorithms in learning and control

Emiland, Garrabe,; Russo, Giovanni

doi:10.1016/j.arcontrol.2022.09.003

Cited by 10 publications

(3 citation statements)

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“…A key feature of our service is that it is built around a framework that intrinsically allows to consider stochastic behaviors, thus explicitly accounting for users’ stochasticity and to e.g., capture their privacy requirements. Specifically, CRAWLING exploits a recent data-driven control algorithm 6 , 7 that returns a randomized behavior for the car (i.e., a probability function) by solving a sequential decision-making problem 8 that formalizes the tracking of a desired behavior expressed in probabilistic terms. That is, our service systematically minimizes a cost function consisting of: (i) a reward capturing road/traffic conditions; (ii) a regularizer that biases the solution towards some target behavior, allowing CRAWLING to keep into account the possible preferences of passengers.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, decision engines 50 have been recently designed that enable agents to make decisions by merging streams of available data. The engines, which leverage the probabilistic framework 8 also used in this paper, have been also applied to parking management. Finally, data-driven intelligent transportation systems, and their data-driven analysis 51 have also gained traction, although heterogeneous datasets, emerging from different information streams that need to be merged, have been singled out as a challenge 52 , 53 .…”

Section: Introductionmentioning

confidence: 99%

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CRAWLING: a crowdsourcing algorithm on wheels for smart parking

Garrabé,

Russo

2023

Sci Rep

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We present the principled design of CRAWLING: a CRowdsourcing Algorithm on WheeLs for smart parkING. CRAWLING is an in-car service for the routing of connected cars. Specifically, cars equipped with our service are able to crowdsource data from third-parties, including other cars, pedestrians, smart sensors and social media, in order to fulfill a given routing task. CRAWLING relies on a solid control-theoretical formulation and the routes it computes are the solution of an optimal data-driven control problem where cars maximize a reward capturing environmental conditions while tracking some desired behavior. A key feature of our service is that it allows to consider stochastic behaviors, while taking into account streams of heterogeneous data. We propose a stand-alone, general-purpose, architecture of CRAWLING and we show its effectiveness on a set of scenarios aimed at illustrating all the key features of our service. Simulations show that, when cars are equipped with CRAWLING, the service effectively orchestrates the vehicles, making them able to react online to road conditions, minimizing their cost functions. The architecture implementing our service is openly available and modular with the supporting code enabling researchers to build on CRAWLING and to replicate the numerical results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CRAWLING: a crowdsourcing algorithm on wheels for smart parking

Garrabé,

Russo

2023

Sci Rep

Self Cite

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“…divergence minimization and we refer to e.g., [15], [16] for examples across learning and control that involve minimizing this functional. Further, the study of mechanisms enabling agents to re-use data, also arises in the design of prediction algorithms from experts [17] and of learning algorithms from multiple simulators [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Decision-Making for Autonomous Agents via Data Composition

Emiland¹,

Lamberti²,

Russo³

2023

Preprint

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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.

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