# Inverse reinforcement learning in continuous time and space

arXiv: https://arxiv.org/abs/1801.07663 · HTML: https://mlsanity.com/paper/1801.07663/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 23 Jan 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, 1 of 1)

- [Online Output-Feedback Parameter and State Estimation for Second Order Linear Systems](https://mlsanity.com/paper/1609.05879/) (2016)

## Built on by (most cited first, 5 of 5)

- [Online inverse reinforcement learning with unknown disturbances](https://mlsanity.com/paper/2003.03912/) (2020)
- [Online Observer-Based Inverse Reinforcement Learning](https://mlsanity.com/paper/2011.02057/) (2020)
- [Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems](https://mlsanity.com/paper/2007.03481/) (2020)
- [Online inverse reinforcement learning with limited data](https://mlsanity.com/paper/2008.08972/) (2020)
- [Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems](https://mlsanity.com/paper/2503.09090/) (2025)

## Abstract (arXiv)

This paper develops a data-driven inverse reinforcement learning technique for a class of linear systems to estimate the cost function of an agent online, using input-output measurements. A simultaneous state and parameter estimator is utilized to facilitate output-feedback inverse reinforcement learning, and cost function estimation is achieved up to multiplication by a constant.