mlsanityData to 14 Oct 2026

Online inverse reinforcement learning for nonlinear systems

arXiv 1811.10471 ↗First posted 21 Nov 2018eess.SY cs.SYcited by 0

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

Abstract (arXiv)

This paper focuses on the development of an online inverse reinforcement learning (IRL) technique for a class of nonlinear systems. The developed approach utilizes observed state and input trajectories, and determines the unknown cost function and the unknown value function online. A parameter estimation technique is utilized to allow the developed IRL technique to determine the cost function weights in the presence of unknown dynamics. Simulation results are presented for a nonlinear system showing convergence of both unknown reward function weights and unknown dynamics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 4 of its 32 references that are in the map. First posted on arXiv 21 Nov 2018.

Builds on

4 of 32 references.

References · 4

  1. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 176
  2. 2015Concurrent learning for parameter estimation using dynamic state-derivative estimatorscited by 7
  3. 2017Online Simultaneous State and Parameter Estimation for Second-order Nonlinear Systemscited by 2
  4. 2015Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivativescited by 1

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  1. 2020Online inverse reinforcement learning with unknown disturbances
  2. 2020Online inverse reinforcement learning with limited data
  3. 2025Adaptive Inverse Reinforcement Learning with Online Off-Policy Data Collection
  4. 2020Online Observer-Based Inverse Reinforcement Learning
  5. 2018Inverse reinforcement learning in continuous time and space
  6. 2018A Framework and Method for Online Inverse Reinforcement Learning
  7. 2025Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems
  8. 2021Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees