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Found 7 papers, 0 papers with code
Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC
In this paper, we propose a learning-based Model Predictive Control (MPC) approach for the polytopic Linear Parameter-Varying (LPV) systems with inexact scheduling parameters (as exogenous signals with inexact bounds), where the Linear Time Invariant (LTI) models (vertices) captured by combinations of the scheduling parameters becomes wrong.
Quasi-Newton Iteration in Deterministic Policy Gradient
We show that the approximate Hessian converges to the exact Hessian at the optimal policy, and allows for a superlinear convergence in the learning, provided that the policy parametrization is rich.
Backstepping-based Integral Sliding Mode Control with Time Delay Estimation for Autonomous Underwater Vehicles
The aim of this paper is to propose a high performance control approach for trajectory tracking of Autonomous Underwater Vehicles (AUVs).
Approximate Robust NMPC using Reinforcement Learning
We present a Reinforcement Learning-based Robust Nonlinear Model Predictive Control (RL-RNMPC) framework for controlling nonlinear systems in the presence of disturbances and uncertainties.
Bias Correction in Deterministic Policy Gradient Using Robust MPC
In this paper, we discuss the deterministic policy gradient using the Actor-Critic methods based on the linear compatible advantage function approximator, where the input spaces are continuous.
Reinforcement Learning based on Scenario-tree MPC for ASVs
A scenario-tree robust MPC is used to handle potential failures of the ship thrusters.
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics
This paper proposes an observer-based framework for solving Partially Observable Markov Decision Processes (POMDPs) when an accurate model is not available.
