Matteo Aicardi

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 under partial observability.