Reinforcement Learning of the Prediction Horizon in Model Predictive Control

• URL: http://arxiv.org/abs/2102.11122v1
• Date: Mon, 22 Feb 2021 15:52:32 GMT
• Title: Reinforcement Learning of the Prediction Horizon in Model Predictive Control
• Authors: Eivind B{\o}hn, Sebastien Gros, Signe Moe, Tor Arne Johansen
• Abstract summary: We propose to learn the optimal prediction horizon as a function of the state using reinforcement learning (RL) We show how the RL learning problem can be formulated and test our method on two control tasks, showing clear improvements over the fixed horizon MPC scheme.
• Score: 1.536989504296526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model predictive control (MPC) is a powerful trajectory optimization control technique capable of controlling complex nonlinear systems while respecting system constraints and ensuring safe operation. The MPC's capabilities come at the cost of a high online computational complexity, the requirement of an accurate model of the system dynamics, and the necessity of tuning its parameters to the specific control application. The main tunable parameter affecting the computational complexity is the prediction horizon length, controlling how far into the future the MPC predicts the system response and thus evaluates the optimality of its computed trajectory. A longer horizon generally increases the control performance, but requires an increasingly powerful computing platform, excluding certain control applications.The performance sensitivity to the prediction horizon length varies over the state space, and this motivated the adaptive horizon model predictive control (AHMPC), which adapts the prediction horizon according to some criteria. In this paper we propose to learn the optimal prediction horizon as a function of the state using reinforcement learning (RL). We show how the RL learning problem can be formulated and test our method on two control tasks, showing clear improvements over the fixed horizon MPC scheme, while requiring only minutes of learning.

Related papers

• Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems [4.05766189327054]
  Hamiltonian-Informed Optimal Neural (Hion) controllers are a novel class of neural network-based controllers for dynamical systems. Hion controllers estimate future states and compute optimal control inputs using Pontryagin's Principle.
  arXiv  Detail & Related papers  (2024-11-02T16:06:29Z)
• Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System [0.7499722271664147]
  This study conducts a comparative analysis of Model Predictive Control (MPC) and Proximal Policy Optimization (PPO), a Deep Reinforcement Learning (DRL) algorithm, applied to a Quanser Aero 2 system. PPO excels in rise-time and adaptability, making it a promising approach for applications requiring rapid response and adaptability.
  arXiv  Detail & Related papers  (2024-08-28T08:35:34Z)
• Reinforced Model Predictive Control via Trust-Region Quasi-Newton Policy Optimization [0.0]
  We propose a Quasi-Newton training algorithm for policy optimization with a superlinear convergence rate. A simulation study illustrates that the proposed training algorithm outperforms other algorithms in terms of data efficiency and accuracy.
  arXiv  Detail & Related papers  (2024-05-28T09:16:08Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• Reinforcement Learning with Model Predictive Control for Highway Ramp Metering [14.389086937116582]
  This work explores the synergy between model-based and learning-based strategies to enhance traffic flow management. The control problem is formulated as an RL task by crafting a suitable stage cost function. An MPC-based RL approach, which leverages the MPC optimal problem as a function approximation for the RL algorithm, is proposed to learn to efficiently control an on-ramp.
  arXiv  Detail & Related papers  (2023-11-15T09:50:54Z)
• Sparsity in Partially Controllable Linear Systems [56.142264865866636]
  We study partially controllable linear dynamical systems specified by an underlying sparsity pattern. Our results characterize those state variables which are irrelevant for optimal control.
  arXiv  Detail & Related papers  (2021-10-12T16:41:47Z)
• Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedules [0.0]
  Model predictive control (MPC) is a method to formulate the optimal scheduling problem for grid flexibilities in a mathematical manner. MPC methods promise accurate results for time-constrained grid optimization but they are inherently limited by the calculation time needed for large and complex power system models. A Neural Predictive Control scheme is proposed to learn optimal control policies for linear and nonlinear power systems through imitation.
  arXiv  Detail & Related papers  (2021-08-19T15:12:35Z)
• Regret-optimal Estimation and Control [52.28457815067461]
  We show that the regret-optimal estimator and regret-optimal controller can be derived in state-space form. We propose regret-optimal analogs of Model-Predictive Control (MPC) and the Extended KalmanFilter (EKF) for systems with nonlinear dynamics.
  arXiv  Detail & Related papers  (2021-06-22T23:14:21Z)
• Anticipating the Long-Term Effect of Online Learning in Control [75.6527644813815]
  AntLer is a design algorithm for learning-based control laws that anticipates learning. We show that AntLer approximates an optimal solution arbitrarily accurately with probability one.
  arXiv  Detail & Related papers  (2020-07-24T07:00:14Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.