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Matteo Aicardi

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Open access 2026

Hierarchical Reinforcement Learning Control of a Quadruple Tank Plant Under Partial Observability

Experimental results in simulation and on real hardware demonstrate that the decentralized–supervised architecture can achieve comparable or improved aggregate tracking performance relative to a centralized policy, while preserving decentralized proposal generation and enabling execution-time supervisory coordination u...

A. Bozzi, Matteo Aicardi, E. Zero et al. · 0 citations

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