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We present the principled design of a control pipeline for the synthesis ofpolicies from examples data. The pipeline, based on a discretized design whichwe term as discrete fully probabilistic design, expounds an algorithm recentlyintroduced in Gagliardi and Russo (2021) to synthesize policies from examplesfor constrained, stochastic and nonlinear systems. Contrary to otherapproaches, the pipeline we present: (i) does not need the constraints to befulfilled in the possibly noisy example data; (ii) enables control synthesiseven when the data are collected from an example system that is different fromthe one under control. The design is benchmarked numerically on an example thatinvolves controlling an inverted pendulum with actuation constraints startingfrom data collected from a physically different pendulum that does not satisfythe system-specific actuation constraints. We also make our fully documentedcode openly available.

Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples