Author

Andrea Manzoni

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Preprint Jul 2026

Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning

A reinforcement learning approach using the TD3 algorithm to train autonomous agents to reach arbitrary targets within a parametric, chaotic double-gyre flow is presented and a trade-off in sensor utility is revealed: velocity-aware agents optimize energy efficiency, whereas vorticity sensors provide superior structural mapping and achieve better target proximity.

Andrea Braghin, Nicolò Botteghi, Matteo Tomasetto et al. · 0 citations

Preprint Jul 2026

Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems

PEARL employs an actor-adjoint algorithm that leverages automatic differentiation to compute policy gradients over short horizons and adjoint-based sensitivities of future returns approximated via neural networks, significantly reducing the number of environment interactions, while mitigating long-term gradient instabilities.

Matteo Tomasetto, Nicolò Botteghi, Gabriele Bruni et al. · 0 citations

Preprint Jul 2026

Real-time optimal control with shallow recurrent decoder networks

This work uses SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize a real-time closed-loop controller for high-dimensional and parametric dynamics, relying solely on limited state sensor readings, alleviating the curse of dimensionality.

Matteo Tomasetto, Francesco Braghin, J. Kutz et al. · 0 citations