# Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/2210.16299 · HTML: https://mlsanity.com/paper/2210.16299/

## 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 47 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## 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)

- [First-Person Activity Forecasting with Online Inverse Reinforcement Learning](https://mlsanity.com/paper/1612.07796/) (2016)
- [Online Observer-Based Inverse Reinforcement Learning](https://mlsanity.com/paper/2011.02057/) (2020)

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

- [Pilot Performance modeling via observer-based inverse reinforcement learning](https://mlsanity.com/paper/2307.13150/) (2023)

## Abstract (arXiv)

A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix, and convergence to such solutions. While offline algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique.