mlsanityData to 7 Oct 2026

Online Observer-Based Inverse Reinforcement Learning

arXiv 2011.02057 ↗First posted 3 Nov 2020eess.SY cs.LG cs.SYcited by 4

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

In this paper, a novel approach to the output-feedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem. Two observer-based techniques for IRL are developed, including a novel observer method that re-uses previous state estimates via history stacks. Theoretical guarantees for convergence and robustness are established under appropriate excitation conditions. Simulations demonstrate the performance of the developed observers and filters under noisy and noise-free measurements.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 31 references that are in the map. First posted on arXiv 3 Nov 2020.

Builds on

9 of 31 references.

Closest ancestors · 5

Same direction, newest first

  1. 2020Online inverse reinforcement learning with limited data18 Aug
  2. 2020Online inverse reinforcement learning with unknown disturbances9 Mar
  3. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations12 Apr
  4. 2018Inverse reinforcement learning in continuous time and space23 Jan
  5. 2018Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations7 Jan

Other references · 4

  1. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
  2. 2018Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learningcited by 21
  3. 2016Inverse Reinforcement Learning in Swarm Systemscited by 18
  4. 2016Online Output-Feedback Parameter and State Estimation for Second Order Linear Systemscited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2022Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learningcited by 1
  2. 2023Pilot Performance modeling via observer-based inverse reinforcement learningcited by 1
  3. 2023Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Gamescited by 0
  4. 2025Recursive Deep Inverse Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2018Online inverse reinforcement learning for nonlinear systems
  2. 2018A Framework and Method for Online Inverse Reinforcement Learning
  3. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  4. 2021Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
  5. 2026Data-Driven Inverse Reinforcement Learning of Linear Systems with Model Uncertainty: A Convex Optimization View
  6. 2025Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems
  7. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  8. 2025Inverse Reinforcement Learning via Convex Optimization