# Pilot Performance modeling via observer-based inverse reinforcement learning

arXiv: https://arxiv.org/abs/2307.13150 · HTML: https://mlsanity.com/paper/2307.13150/

## Key facts

- Cited by 1 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 2 of its 34 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Placed in

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

## Builds on (most cited first, 2 of 2)

- [Online Observer-Based Inverse Reinforcement Learning](https://mlsanity.com/paper/2011.02057/) (2020)
- [Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning](https://mlsanity.com/paper/2210.16299/) (2022)

## Built on by (most cited first, 1 of 1)

- [ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration](https://mlsanity.com/paper/2402.19128/) (2024)

## Abstract (arXiv)

The focus of this paper is behavior modeling for pilots of unmanned aerial vehicles. The pilot is assumed to make decisions that optimize an unknown cost functional, which is estimated from observed trajectories using a novel inverse reinforcement learning (IRL) framework. The resulting IRL problem often admits multiple solutions. In this paper, a recently developed novel IRL observer is adapted to the pilot modeling problem. The observer is shown to converge to one of the equivalent solutions of the IRL problem. The developed technique is implemented on a quadcopter where the pilot is a linear quadratic supervisory controller that generates velocity commands for the quadcopter to travel to and hover over a desired location. Experimental results demonstrate the robustness of the method and its ability to learn equivalent cost functionals.