# Online inverse reinforcement learning for nonlinear systems

arXiv: https://arxiv.org/abs/1811.10471 · HTML: https://mlsanity.com/paper/1811.10471/

## 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.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 4 of 4)

- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Concurrent learning for parameter estimation using dynamic state-derivative estimators](https://mlsanity.com/paper/1507.08903/) (2015)
- [Online Simultaneous State and Parameter Estimation for Second-order Nonlinear Systems](https://mlsanity.com/paper/1703.07068/) (2017)
- [Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives](https://mlsanity.com/paper/1512.03464/) (2015)

## 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.