Learning the optimal state-feedback using deep networks
Carlos Sánchez-Sánchez, Dario Izzo, Daniel Hennes
In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, (SSCI-2016), 06.12.-09.12.2016, Athen, IEEE, Dec/2016.
Abstract
:
We investigate the use of deep artificial neural
networks to approximate the optimal state-feedback control of
continuous time, deterministic, non-linear systems. The networks
are trained in a supervised manner using trajectories generated
by solving the optimal control problem via the Hermite-Simpson
transcription method. We find that deep networks are able to
represent the optimal state-feedback with high accuracy and
precision well outside the training area. We consider non-linear
dynamical models under different cost functions that result in
both smooth and discontinuous (bang-bang) optimal control
solutions. In particular, we investigate the inverted pendulum
swing-up and stabilization, a multicopter pin-point landing and
a spacecraft free landing problem. Across all domains, we find
that deep networks significantly outperform shallow networks in
the ability to build an accurate functional representation of the
optimal control. In the case of spacecraft and multicopter landing,
deep networks are able to achieve safe landings consistently even
when starting well outside of the training area.
Files:
20161220_Learning_the_optimal_state-feedback_using_deep_networks.pdf
Links:
https://ieeexplore.ieee.org/document/7850105