Author

Alberto Giammarino

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Jul 2026

Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping

This work presents Feasible Action for Optimal Control (FAOC), a novel control framework integrating Reinforcement Learning (RL) and Optimal Control (OC), with a computationally efficient, optimization-based mapping algorithm that transforms the RL agent's action from a static abstract set into a state-dependent feasible parameter set of the Optimal Control problem.

Stefan Richter, Alberto Giammarino, G. Torrente et al. · arXiv.org · 0 citations