mlsanityData to 14 Oct 2026

Inverse reinforcement learning by expert imitation for the stochastic linear-quadratic optimal control problem

arXiv 2405.17085 ↗First posted 27 May 2024math.OCcited by 3

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

Abstract (arXiv)

This article studies inverse reinforcement learning (IRL) for the stochastic linear-quadratic optimal control problem, where two agents are considered. A learner agent does not know the expert agent's performance cost function, but it imitates the behavior of the expert agent by constructing an underlying cost function that obtains the same optimal feedback control as the expert's. We first develop a model-based IRL algorithm, which consists of a policy correction and a policy update from the policy iteration in reinforcement learning, as well as a cost function weight reconstruction based on the inverse optimal control. Then, under this scheme, we propose a model-free off-policy IRL algorithm, which does not need to know or identify the system and only needs to collect the behavior data of the expert agent and learner agent once during the iteration process. Moreover, the proofs of the algorithm's convergence, stability, and non-unique solutions are given. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithm.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 47 references that are in the map. First posted on arXiv 27 May 2024.

Builds on

9 of 47 references.

References · 9

  1. 2020Sequential Anomaly Detection using Inverse Reinforcement Learningcited by 20
  2. 2020Robust Policy Iteration for Continuous-time Linear Quadratic Regulationcited by 15
  3. 2020Stochastic Linear Quadratic Optimal Control Problem: A Reinforcement Learning Methodcited by 11
  4. 2021Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systemscited by 8
  5. 2018Continuous-Time Robust Dynamic Programmingcited by 4
  6. 2020Inverse stochastic optimal controlscited by 3
  7. 2023Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Gamescited by 2
  8. 2023Inverse optimal control for averaged cost per stage linear quadratic regulatorscited by 2
  9. 2022Inverse Stochastic Optimal Control for Linear-Quadratic Gaussian and Linear-Quadratic Sensorimotor Control Modelscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026SCaLE: Switching Cost aware Learning and Explorationcited by 0
  2. 2026Inverse Optimal Control for Linear Quadratic Problem with Poisson Jumps: Model-Free Inverse Reinforcement Learning Approachescited by 0
  3. 2026Inverse reinforcement learning for indefinite mean-field social optimization with multiplicative noisecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  2. 2023Inverse Reinforcement Learning without Reinforcement Learning
  3. 2024Hybrid Inverse Reinforcement Learning
  4. 2024Inverse Reinforcement Learning with Sub-optimal Experts
  5. 2023Curricular Subgoals for Inverse Reinforcement Learning
  6. 2018A Framework and Method for Online Inverse Reinforcement Learning
  7. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  8. 2026Data-Driven Inverse Reinforcement Learning of Linear Systems with Model Uncertainty: A Convex Optimization View