mlsanityData to 14 Oct 2026

Model-free H∞ control of Ito stochastic system via off-policy reinforcement learning

arXiv 2403.04412 ↗First posted 7 Mar 2024math.OCcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

The stochastic H∞ control is studied for a linear stochastic Ito system with an unknown system model. The linear stochastic H∞ control issue is known to be transformable into the problem of solving a so-called generalized algebraic Riccati equation (GARE), which is a nonlinear equation that is typically difficult to solve analytically. Worse, model-based techniques cannot be utilized to approximately solve a GARE when an accurate system model is unavailable or prohibitively expensive to construct in reality. To address these issues, an off-policy reinforcement learning (RL) approach is presented to learn the solution of a GARE from real system data rather than a system model; its convergence is demonstrated, and the robustness of RL to errors in the learning process is investigated. In the off-policy RL approach, the system data may be created with behavior policies rather than the target policies, which is highly significant and promising for use in actual systems. Finally, the proposed off-policy RL approach is validated on a stochastic linear F-16 aircraft system.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 33 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

3 of 33 references.

References · 3

  1. 2020Robust Policy Iteration for Continuous-time Linear Quadratic Regulationcited by 15
  2. 2021Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systemscited by 8
  3. 2023On stabilizability and exact observability of stochastic systems with their applicationscited by 5

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  1. 2024Robust policy iteration for continuous-time stochastic H_∞ control problem with unknown dynamics
  2. 2024Inverse reinforcement learning by expert imitation for the stochastic linear-quadratic optimal control problem
  3. 2026Inverse Optimal Control for Linear Quadratic Problem with Poisson Jumps: Model-Free Inverse Reinforcement Learning Approaches
  4. 2022A Unified Framework for Alternating Offline Model Training and Policy Learning
  5. 2025Computing stabilizing feedback gains for stochastic linear systems via policy iteration method ↗
  6. 2021Offline RL Without Off-Policy Evaluation
  7. 2025Optimal Regulation of Nonlinear Input-Affine Systems via an Integral Reinforcement Learning-Based State-Dependent Riccati Equation Approach
  8. 2020Model-Based Offline Planning