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Found 9 papers, 0 papers with code
Equivalence of Optimality Criteria for Markov Decision Process and Model Predictive Control
This paper shows that the optimal policy and value functions of a Markov Decision Process (MDP), either discounted or not, can be captured by a finite-horizon undiscounted Optimal Control Problem (OCP), even if based on an inexact model.
Functional Stability of Discounted Markov Decision Processes Using Economic MPC Dissipativity Theory
This paper discusses the functional stability of closed-loop Markov Chains under optimal policies resulting from a discounted optimality criterion, forming Markov Decision Processes (MDPs).
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.
Verification of Dissipativity and Evaluation of Storage Function in Economic Nonlinear MPC using Q-Learning
In the Economic Nonlinear Model Predictive (ENMPC) context, closed-loop stability relates to the existence of a storage function satisfying a dissipation inequality.
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.
MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storage
In this paper, we are interested in optimal control problems with purely economic costs, which often yield optimal policies having a (nearly) bang-bang structure.
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 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.
Reinforcement Learning based on Scenario-tree MPC for ASVs
A scenario-tree robust MPC is used to handle potential failures of the ship thrusters.
