341 citations · 793 across the 35 of their papers we have counts for
6 papers · 1 filter
Autonomous docking using direct optimal control
Andreas B. Martinsen, Anastasios M. Lekkas, Sebastien Gros
We propose a method for performing autonomous docking of marine vessels using numerical optimal control. The task is framed as a dynamic positioning problem, with the addition of s…
Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part I - Stochastic case
Sebastien Gros, Mario Zanon
We present a methodology to deploy the stochastic policy gradient method, using actor-critic techniques, when the optimal policy is approximated using a parametric optimization pro…
Towards Safe Reinforcement Learning Using NMPC and Policy Gradients: Part II - Deterministic Case
Sebastien Gros, Mario Zanon
In this paper, we present a methodology to deploy the deterministic policy gradient method, using actor-critic techniques, when the optimal policy is approximated using a parametri…
Safe Reinforcement Learning Using Robust MPC
Mario Zanon, Sébastien Gros
Reinforcement Learning (RL) has recently impressed the world with stunning results in various applications. While the potential of RL is now well-established, many critical aspects…
Practical Reinforcement Learning of Stabilizing Economic MPC
Mario Zanon, Sébastien Gros, Alberto Bemporad
Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without any prior knowledge of the process to be controlled. Model Predictive Control (MP…
Data-driven Economic NMPC using Reinforcement Learning
Sébastien Gros, Mario Zanon
Reinforcement Learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on…